### Archive

Posts Tagged ‘treatment effects’

## Using mlexp to estimate endogenous treatment effects in a heteroskedastic probit model

I use features new to Stata 14.1 to estimate an average treatment effect (ATE) for a heteroskedastic probit model with an endogenous treatment. In 14.1, we added new prediction statistics after mlexp that margins can use to estimate an ATE.

I am building on a previous post in which I demonstrated how to use mlexp to estimate the parameters of a probit model with an endogenous treatment and used margins to estimate the ATE for the model Using mlexp to estimate endogenous treatment effects in a probit model. Currently, no official commands estimate the heteroskedastic probit model with an endogenous treatment, so in this post I show how mlexp can be used to extend the models estimated by Stata.

Heteroskedastic probit model

For binary outcome $$y_i$$ and regressors $${\bf x}_i$$, the probit model assumes

$y_i = {\bf 1}({\bf x}_i{\boldsymbol \beta} + \epsilon_i > 0)$

The indicator function $${\bf 1}(\cdot)$$ outputs 1 when its input is true and outputs 0 otherwise. The error $$\epsilon_i$$ is standard normal.

Assuming that the error has constant variance may not always be wise. Suppose we are studying a certain business decision. Large firms, because they have the resources to take chances, may exhibit more variation in the factors that affect their decision than small firms.

In the heteroskedastic probit model, regressors $${\bf w}_i$$ determine the variance of $$\epsilon_i$$. Following Harvey (1976), we have

$\mbox{Var}\left(\epsilon_i\right) = \left\{\exp\left({\bf w}_i{\boldsymbol \gamma}\right)\right\}^2 \nonumber$

Heteroskedastic probit model with treatment

In this section, I review the potential-outcome framework used to define an ATE and extend it for the heteroskedastic probit model. For each treatment level, there is an outcome that we would observe if a person were to select that treatment level. When the outcome is binary and there are two treatment levels, we can specify how the potential outcomes $$y_{0i}$$ and $$y_{1i}$$ are generated from the regressors $${\bf x}_i$$ and the error terms $$\epsilon_{0i}$$ and $$\epsilon_{1i}$$:

$\begin{eqnarray*} y_{0i} &=& {\bf 1}({\bf x}_i{\boldsymbol \beta}_0 + \epsilon_{0i} > 0) \cr y_{1i} &=& {\bf 1}({\bf x}_i{\boldsymbol \beta}_1 + \epsilon_{1i} > 0) \end{eqnarray*}$

We assume a heteroskedastic probit model for the potential outcomes. The errors are normal with mean $$0$$ and conditional variance generated by regressors $${\bf w}_i$$. In this post, we assume equal variance of the potential outcome errors.

$\mbox{Var}\left(\epsilon_{0i}\right) = \mbox{Var}\left(\epsilon_{1i}\right) = \left\{\exp\left({\bf w}_i{\boldsymbol \gamma}\right)\right\}^2 \nonumber$

The heteroskedastic probit model for potential outcomes $$y_{0i}$$ and $$y_{1i}$$ with treatment $$t_i$$ assumes that we observe the outcome

$y_i = (1-t_i) y_{0i} + t_i y_{1i} \nonumber$

So we observe $$y_{1i}$$ under the treatment ($$t_{i}=1$$) and $$y_{0i}$$ when the treatment is withheld ($$t_{i}=0$$).

The treatment $$t_i$$ is determined by regressors $${\bf z}_i$$ and error $$u_i$$:

$t_i = {\bf 1}({\bf z}_i{\boldsymbol \psi} + u_i > 0) \nonumber$

The treatment error $$u_i$$ is normal with mean zero, and we allow its variance to be determined by another set of regressors $${\bf v}_i$$:

$\mbox{Var}\left(u_i\right) = \left\{\exp\left({\bf v}_i{\boldsymbol \alpha}\right)\right\}^2 \nonumber$

Heteroskedastic probit model with endogenous treatment

In the previous post, I described how to model endogeneity for the treatment $$t_i$$ by correlating the outcome errors $$\epsilon_{0i}$$ and $$\epsilon_{1i}$$ with the treatment error $$u_i$$. We use the same framework for modeling endogeneity here. The variance of the errors may change depending on the heteroskedasticity regressors $${\bf w}_i$$ and $${\bf v}_i$$, but their correlation remains constant. The errors $$\epsilon_{0i}$$, $$\epsilon_{1i}$$, and $$u_i$$ are trivariate normal with correlation

$\left[\begin{matrix} 1 & \rho_{01} & \rho_{t} \cr \rho_{01} & 1 & \rho_{t} \cr \rho_{t} & \rho_{t} & 1 \end{matrix}\right] \nonumber$

Now we have all the pieces we need to write the log likelihood of the heteroskedastic probit model with an endogenous treatment. The form of the likelihood is similar to what was given in the previous post. Now the inputs to the bivariate normal cumulative distribution function, $$\Phi_2$$, are standardized by dividing by the conditional standard deviations of the errors.

The log likelihood for observation $$i$$ is

$\begin{eqnarray*} \ln L_i = & & {\bf 1}(y_i =1 \mbox{ and } t_i = 1) \ln \Phi_2\left\{\frac{{\bf x}_i{\boldsymbol \beta}_1}{\exp\left({\bf w}_i{\boldsymbol \gamma}\right)}, \frac{{\bf z}_i{\boldsymbol \psi}}{\exp\left({\bf v}_i{\boldsymbol \alpha}\right)},\rho_t\right\} + \cr & & {\bf 1}(y_i=0 \mbox{ and } t_i=1)\ln \Phi_2\left\{\frac{-{\bf x}_i{\boldsymbol \beta}_1}{\exp\left({\bf w}_i{\boldsymbol \gamma}\right)}, \frac{{\bf z}_i{\boldsymbol \psi}}{\exp\left({\bf v}_i{\boldsymbol \alpha}\right)},-\rho_t\right\} + \cr & & {\bf 1}(y_i=1 \mbox{ and } t_i=0) \ln \Phi_2\left\{\frac{{\bf x}_i{\boldsymbol \beta}_0}{\exp\left({\bf w}_i{\boldsymbol \gamma}\right)}, \frac{-{\bf z}_i{\boldsymbol \psi}}{\exp\left({\bf v}_i{\boldsymbol \alpha}\right)},-\rho_t\right\} + \cr & & {\bf 1}(y_i=0 \mbox{ and } t_i = 0)\ln \Phi_2\left\{\frac{-{\bf x}_i{\boldsymbol \beta}_0}{\exp\left({\bf w}_i{\boldsymbol \gamma}\right)}, \frac{-{\bf z}_i{\boldsymbol \psi}}{\exp\left({\bf v}_i{\boldsymbol \alpha}\right)},\rho_t\right\} \end{eqnarray*}$

The data

We will simulate data from a heteroskedastic probit model with an endogenous treatment and then estimate the parameters of the model with mlexp. Then, we will use margins to estimate the ATE.

. set seed 323

. set obs 10000
number of observations (_N) was 0, now 10,000

. generate x = .8*rnormal() + 4

. generate b = rpoisson(1)

. generate z = rnormal()

. matrix cm = (1, .3,.7 \ .3, 1, .7 \ .7, .7, 1)

. drawnorm ey0 ey1 et, corr(cm)


We simulate a random sample of 10,000 observations. The treatment and outcome regressors are generated in a similar manner to their creation in the last post. As in the last post, we generate the errors with drawnorm to have correlation $$0.7$$.

. generate g = runiform()

. generate h = rnormal()

. quietly replace ey0 = ey0*exp(.5*g)

. quietly replace ey1 = ey1*exp(.5*g)

. quietly replace et = et*exp(.1*h)

. generate t = .5*x - .1*b + .5*z - 2.4 + et > 0

. generate y0 = .6*x - .8 + ey0 > 0

. generate y1 = .3*x - 1.3 + ey1 > 0

. generate y = (1-t)*y0 + t*y1


The uniform variable g is generated as a regressor for the outcome error variance, while h is a regressor for the treatment error variance. We scale the errors by using the variance regressors so that they are heteroskedastic, and then we generate the treatment and outcome indicators.

Estimating the model parameters

Now, we will use mlexp to estimate the parameters of the heteroskedastic probit model with an endogenous treatment. As in the previous post, we use the cond() function to calculate different values of the likelihood based on the different values of $$y$$ and $$t$$. We use the factor-variable operator ibn on $$t$$ in equation y to allow for a different intercept at each level of $$t$$. An interaction between $$t$$ and $$x$$ is also specified in equation y. This allows for a different coefficient on $$x$$ at each level of $$t$$.

. mlexp (ln(cond(t, ///
>         cond(y,binormal({y: i.t#c.x ibn.t}/exp({g:g}), ///
>             {t: x b z _cons}/exp({h:h}),{rho}), ///
>                 binormal(-{y:}/exp({g:}),{t:}/exp({h:}),-{rho})), ///
>         cond(y,binormal({y:}/exp({g:}),-{t:}/exp({h:}),-{rho}), ///
>                 binormal(-{y:}/exp({g:}),-{t:}/exp({h:}),{rho}) ///
>         )))), vce(robust)

initial:       log pseudolikelihood = -13862.944
alternative:   log pseudolikelihood = -16501.619
rescale:       log pseudolikelihood = -13858.877
rescale eq:    log pseudolikelihood = -11224.877
Iteration 0:   log pseudolikelihood = -11224.877  (not concave)
Iteration 1:   log pseudolikelihood = -10644.625
Iteration 2:   log pseudolikelihood = -10074.998
Iteration 3:   log pseudolikelihood = -9976.6027
Iteration 4:   log pseudolikelihood = -9973.0988
Iteration 5:   log pseudolikelihood = -9973.0913
Iteration 6:   log pseudolikelihood = -9973.0913

Maximum likelihood estimation

Log pseudolikelihood = -9973.0913               Number of obs     =     10,000

------------------------------------------------------------------------------
|               Robust
|      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
y            |
t#c.x |
0  |   .6178115   .0334521    18.47   0.000     .5522467    .6833764
1  |   .2732094   .0365742     7.47   0.000     .2015253    .3448936
|
t |
0  |  -.8403294   .1130197    -7.44   0.000    -1.061844   -.6188149
1  |  -1.215177   .1837483    -6.61   0.000    -1.575317   -.8550371
-------------+----------------------------------------------------------------
g            |
g |   .4993187   .0513297     9.73   0.000     .3987143    .5999232
-------------+----------------------------------------------------------------
t            |
x |   .4985802   .0183033    27.24   0.000     .4627065    .5344539
b |  -.1140255   .0132988    -8.57   0.000    -.1400908   -.0879603
z |   .4993995   .0150844    33.11   0.000     .4698347    .5289643
_cons |  -2.402772   .0780275   -30.79   0.000    -2.555703   -2.249841
-------------+----------------------------------------------------------------
h            |
h |   .1011185   .0199762     5.06   0.000     .0619658    .1402713
-------------+----------------------------------------------------------------
/rho |   .7036964   .0326734    21.54   0.000     .6396577    .7677351
------------------------------------------------------------------------------


Our parameter estimates are close to their true values.

Estimating the ATE

The ATE of $$t$$ is the expected value of the difference between $$y_{1i}$$ and $$y_{0i}$$, the average difference between the potential outcomes. Using the law of iterated expectations, we have

$\begin{eqnarray*} E(y_{1i}-y_{0i})&=& E\left\{ E\left(y_{1i}-y_{0i}|{\bf x}_i,{\bf w}_i\right)\right\} \cr &=& E\left\lbrack\Phi\left\{\frac{{\bf x}_i{\boldsymbol \beta}_1}{ \exp\left({\bf w}_i{\boldsymbol \gamma}\right)}\right\}- \Phi\left\{\frac{{\bf x}_i{\boldsymbol \beta}_0}{ \exp\left({\bf w}_i{\boldsymbol \gamma}\right)}\right\}\right\rbrack \cr \end{eqnarray*}$

This can be estimated as a mean of predictions.

Now, we estimate the ATE by using margins. We specify the normal probability expression in the expression() option. We use the expression function xb() to get the linear predictions for the outcome equation and the outcome error variance equation. We can now predict these linear forms after mlexp in Stata 14.1. We specify r.t so that margins will take the difference of the expression under t=1 and t=0. We specify vce(unconditional) to obtain standard errors for the population ATE rather than the sample ATE; we specified vce(robust) for mlexp so that we could specify vce(unconditional) for margins. The contrast(nowald) option is specified to omit the Wald test for the difference.

. margins r.t, expression(normal(xb(y)/exp(xb(g)))) ///
>     vce(unconditional) contrast(nowald)

Contrasts of predictive margins

Expression   : normal(xb(y)/exp(xb(g)))

--------------------------------------------------------------
|            Unconditional
|   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
t |
(1 vs 0)  |  -.4183043   .0202635     -.4580202   -.3785885
--------------------------------------------------------------


We estimate that the ATE of $$t$$ on $$y$$ is $$-0.42$$. So taking the treatment decreases the probability of a positive outcome by $$0.42$$ on average over the population.

We will compare this estimate to the average difference of $$y_{1}$$ and $$y_{0}$$ in the sample. We can do this because we simulated the data. In practice, only one potential outcome is observed for every observation, and this average difference cannot be computed.

. generate diff = y1 - y0

. sum diff

Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
diff |     10,000      -.4164    .5506736         -1          1


In our sample, the average difference of $$y_{1}$$ and $$y_{0}$$ is also $$-0.42$$.

Conclusion

I have demonstrated how to estimate the parameters of a model that is not available in Stata: the heteroskedastic probit model with an endogenous treatment using mlexp. See [R] mlexp for more details about mlexp. I have also demonstrated how to use margins to estimate the ATE for the heteroskedastic probit model with an endogenous treatment. See [R] margins for more details about mlexp.

Reference

Harvey, A. C. 1976. Estimating regression models with multiplicative heteroscedasticity. Econometrica 44: 461-465.

Categories: Statistics Tags:

## Using mlexp to estimate endogenous treatment effects in a probit model

I use features new to Stata 14.1 to estimate an average treatment effect (ATE) for a probit model with an endogenous treatment. In 14.1, we added new prediction statistics after mlexp that margins can use to estimate an ATE.

I am building on a previous post in which I demonstrated how to use mlexp to estimate the parameters of a probit model with sample selection. Our results match those obtained with biprobit; see [R] biprobit for more details. In a future post, I use these techniques to estimate treatment-effect parameters not yet available from another Stata command.

Probit model with treatment

In this section, I describe the potential-outcome framework used to define an ATE. For each treatment level, there is an outcome that we would observe if a person were to select that treatment level. When the outcome is binary and there are two treatment levels, we can specify how the potential outcomes $$y_{0i}$$ and $$y_{1i}$$ are generated from the regressors $${\bf x}_i$$ and the error terms $$\epsilon_{0i}$$ and $$\epsilon_{1i}$$:

$\begin{eqnarray*} y_{0i} &=& {\bf 1}({\bf x}_i{\boldsymbol \beta}_0 + \epsilon_{0i} > 0) \cr y_{1i} &=& {\bf 1}({\bf x}_i{\boldsymbol \beta}_1 + \epsilon_{1i} > 0) \end{eqnarray*}$

(Assuming that each error is standard normal, this gives us a bivariate probit model.) The indicator function $${\bf 1}(\cdot)$$ outputs 1 when its input is true and 0 otherwise.

The probit model for potential outcomes $$y_{0i}$$ and $$y_{1i}$$ with treatment $$t_i$$ assumes that we observe the outcome

$y_i = (1-t_i) y_{0i} + t_i y_{1i} \nonumber$

So we observe $$y_{1i}$$ under the treatment ($$t_{i}=1$$) and $$y_{0i}$$ when the treatment is withheld ($$t_{i}=0$$).

The treatment $$t_i$$ is determined by regressors $${\bf z}_i$$ and standard normal error $$u_i$$:

$t_i = {\bf 1}({\bf z}_i{\boldsymbol \psi} + u_i > 0) \nonumber$

Probit model with endogenous treatment

We could estimate the parameters $${\boldsymbol \beta}_0$$ and $${\boldsymbol \beta}_1$$ using a probit regression on $$y_i$$ if $$t_i$$ was not related to the unobserved errors $$\epsilon_{0i}$$ and $$\epsilon_{1i}$$. This may not always be the case. Suppose we modeled whether parents send their children to private school and used private tutoring for the child as a treatment. Unobserved factors that influence private school enrollment may be correlated with the unobserved factors that influence whether private tutoring is given. The treatment would be correlated with the unobserved errors of the outcome.

We can treat $$t_i$$ as endogenous by allowing $$\epsilon_{0i}$$ and $$\epsilon_{1i}$$ to be correlated with $$u_i$$. In this post, we will assume that these correlations are the same. Formally, $$\epsilon_{0i}$$, $$\epsilon_{1i}$$, and $$u_i$$ are trivariate normal with covariance:

$\left[\begin{matrix} 1 & \rho_{01} & \rho_{t} \cr \rho_{01} & 1 & \rho_{t} \cr \rho_{t} & \rho_{t} & 1 \end{matrix}\right] \nonumber$

The correlation $$\rho_{01}$$ cannot be identified because we never observe both $$y_{0i}$$ and $$y_{1i}$$. However, identification of $$\rho_{01}$$ is not necessary to estimate the other parameters, because we will observe the covariates and outcome in observations from each treatment group.

The log-likelihood for observation $$i$$ is

$\begin{eqnarray*} \ln L_i = & & {\bf 1}(y_i =1 \mbox{ and } t_i = 1) \ln \Phi_2({\bf x}_i{\boldsymbol \beta}_1, {\bf z}_i{\boldsymbol \gamma},\rho_t) + \cr & & {\bf 1}(y_i=0 \mbox{ and } t_i=1)\ln \Phi_2(-{\bf x}_i{\boldsymbol \beta}_1, {\bf z}_i{\boldsymbol \gamma},-\rho_t) + \cr & & {\bf 1}(y_i=1 \mbox{ and } t_i=0) \ln \Phi_2({\bf x}_i{\boldsymbol \beta}_0, -{\bf z}_i{\boldsymbol \gamma},-\rho_t) + \cr & & {\bf 1}(y_i=0 \mbox{ and } t_i = 0)\ln \Phi_2(-{\bf x}_i{\boldsymbol \beta}_0, -{\bf z}_i{\boldsymbol \gamma},\rho_t) \end{eqnarray*}$

where $$\Phi_2$$ is the bivariate normal cumulative distribution function.

This model is a variation of the bivariate probit model. For a good introduction to the bivariate probit model, see Pindyck and Rubinfeld (1998).

The data

We will simulate data from a probit model with an endogenous treatment and then estimate the parameters of the model using mlexp. Then, we will use margins to estimate the ATE. We simulate a random sample of 10,000 observations.

. set seed 3211

. set obs 10000
number of observations (_N) was 0, now 10,000

. gen x = rnormal() + 4

. gen b = rpoisson(1)

. gen z = rnormal()


First, we generate the regressors. The variable $$x$$ has a normal distribution with a mean of 4 and variance of 1. It is used as a regressor for the outcome and treatment. The variable $$b$$ has a Poisson distribution with a mean of 1 and will be used as a treatment regressor. A standard normal variable $$z$$ is also used as a treatment regressor.

. matrix cm = (1, .3,.7 \ .3, 1, .7 \ .7, .7, 1)

. drawnorm ey0 ey1 et, corr(cm)

. gen t = .5*x - .1*b + .4*z - 2.4 + et > 0

. gen y0 = .6*x - .8 + ey0 > 0

. gen y1 = .3*x - 1.2 + ey1 > 0

. gen y = (1-t)*y0 + t*y1


Next, we draw the unobserved errors. The potential outcome and treatment errors will have correlation $$.7$$. We generate the errors using the drawnorm command. Finally, the outcome and treatment indicators are created.

Estimating the model parameters

Now, we will use mlexp to estimate the parameters of the probit model with an endogenous treatment. As in the previous post, we use the cond() function to calculate different values of the likelihood based on the different values of $$y$$ and $$t$$. We use the factor variable operator ibn on $$t$$ in equation y to allow for a different intercept at each level of $$t$$. An interaction between $$t$$ and $$x$$ is also specified in equation y. This allows for a different coefficient on $$x$$ at each level of $$t$$. We also specify vce(robust) so that we can use vce(unconditional) when we use margins later.

. mlexp (ln(cond(t,cond(y,binormal({y: i.t#c.x ibn.t},            ///
>                                  {t: x b z _cons}, {rho}),      ///
>                         binormal(-{y:},{t:}, -{rho})),          ///
>                  cond(y,binormal({y:},-{t:},-{rho}),            ///
>                         binormal(-{y:},-{t:},{rho})))))         ///
>         , vce(robust)

initial:       log pseudolikelihood = -13862.944
alternative:   log pseudolikelihood = -15511.071
rescale:       log pseudolikelihood = -13818.369
rescale eq:    log pseudolikelihood = -10510.488
Iteration 0:   log pseudolikelihood = -10510.488  (not concave)
Iteration 1:   log pseudolikelihood = -10004.946
Iteration 2:   log pseudolikelihood = -9487.4032
Iteration 3:   log pseudolikelihood = -9286.0118
Iteration 4:   log pseudolikelihood =  -9183.901
Iteration 5:   log pseudolikelihood = -9181.9207
Iteration 6:   log pseudolikelihood = -9172.0256
Iteration 7:   log pseudolikelihood = -9170.8198
Iteration 8:   log pseudolikelihood = -9170.7994
Iteration 9:   log pseudolikelihood = -9170.7994

Maximum likelihood estimation

Log pseudolikelihood = -9170.7994               Number of obs     =     10,000

------------------------------------------------------------------------------
|               Robust
|      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
y            |
t#c.x |
0  |   .5829362   .0223326    26.10   0.000     .5391651    .6267073
1  |   .2745585   .0259477    10.58   0.000     .2237021     .325415
|
t |
0  |  -.7423227   .0788659    -9.41   0.000     -.896897   -.5877483
1  |  -1.088765   .1488922    -7.31   0.000    -1.380589   -.7969419
-------------+----------------------------------------------------------------
t            |
x |   .4900691   .0148391    33.03   0.000     .4609851    .5191532
b |  -.1086717   .0132481    -8.20   0.000    -.1346375   -.0827059
z |   .4135792   .0150112    27.55   0.000     .3841579    .4430006
_cons |  -2.354418   .0640056   -36.78   0.000    -2.479867   -2.228969
-------------+----------------------------------------------------------------
/rho |   .7146737   .0377255    18.94   0.000     .6407331    .7886143
------------------------------------------------------------------------------


Our parameter estimates are close to their true values.

Estimating the ATE

The ATE of $$t$$ is the expected value of the difference between $$y_{1i}$$ and $$y_{0i}$$, the average difference between the potential outcomes. Using the law of iterated expectations, we have

$\begin{eqnarray*} E(y_{1i}-y_{0i}) &=& E\{E(y_{1i}-y_{0i}|{\bf x}_i)\} \cr &=& E\{\Phi({\bf x}_i{\boldsymbol \beta}_1)- \Phi({\bf x}_i{\boldsymbol \beta}_0)\} \end{eqnarray*}$

This can be estimated as a predictive margin.

Now, we estimate the ATE using margins. We specify the normal probability expression in the expression() option. The xb() term refers to the linear prediction of the first equation, which we can now predict in Stata 14.1. We specify r.t so that margins will take the difference of the expression under $$t=1$$ and $$t=0$$. We specify vce(unconditional) to obtain standard errors for the population ATE rather than the sample ATE. The contrast(nowald) option is specified to omit the Wald test for the difference.

. margins r.t, expression(normal(xb())) vce(unconditional) contrast(nowald)

Contrasts of predictive margins

Expression   : normal(xb())

--------------------------------------------------------------
|            Unconditional
|   Contrast   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
t |
(1 vs 0)  |  -.4112345   .0248909     -.4600197   -.3624493
--------------------------------------------------------------


We estimate that the ATE of $$t$$ on $$y$$ is $$-.41$$. So taking the treatment decreases the probability of a positive outcome by $$.41$$ on average over the population.

We will compare this estimate to the sample difference of $$y_{1}$$ and $$y_{0}$$.

. gen diff = y1 - y0

. sum diff

Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
diff |     10,000      -.4132    .5303715         -1          1


In our sample, the average difference of $$y_{1}$$ and $$y_{0}$$ is also $$-.41$$.

Conclusion

I have demonstrated how to estimate the parameters of a model with a complex likelihood function: the probit model with an endogenous treatment using mlexp. See [R] mlexp for more details about mlexp. I have also demonstrated how to use margins to estimate the ATE for the probit model with an endogenous treatment. See [R] margins for more details about margins.

Reference

Pindyck, R. S., and D. L. Rubinfeld. 1998. Econometric Models and Economic Forecasts. 4th ed. New York: McGraw-Hill.

Categories: Statistics Tags:

## Introduction to treatment effects in Stata: Part 2

This post was written jointly with David Drukker, Director of Econometrics, StataCorp.

In our last post, we introduced the concept of treatment effects and demonstrated four of the treatment-effects estimators that were introduced in Stata 13.  Today, we will talk about two more treatment-effects estimators that use matching.

Introduction

Last time, we introduced four estimators for estimating the average treatment effect (ATE) from observational data.  Each of these estimators has a different way of solving the missing-data problem that arises because we observe only the potential outcome for the treatment level received.  Today, we introduce estimators for the ATE that solve the missing-data problem by matching.

Matching pairs the observed outcome of a person in one treatment group with the outcome of the “closest” person in the other treatment group. The outcome of the closest person is used as a prediction for the missing potential outcome. The average difference between the observed outcome and the predicted outcome estimates the ATE.

What we mean by “closest” depends on our data. Matching subjects based on a single binary variable, such as sex, is simple: males are paired with males and females are paired with females. Matching on two categorical variables, such as sex and race, isn’t much more difficult. Matching on continuous variables, such as age or weight, can be trickier because of the sparsity of the data. It is unlikely that there are two 45-year-old white males who weigh 193 pounds in a sample. It is even less likely that one of those men self-selected into the treated group and the other self-selected into the untreated group. So, in such cases, we match subjects who have approximately the same weight and approximately the same age.

This example illustrates two points. First, there is a cost to matching on continuous covariates; the inability to find good matches with more than one continuous covariate causes large-sample bias in our estimator because our matches become increasingly poor.

Second, we must specify a measure of similarity. When matching directly on the covariates, distance measures are used and the nearest neighbor selected. An alternative is to match on an estimated probability of treatment, known as the propensity score.

Before we discuss estimators for observational data, we note that matching is sometimes used in experimental data to define pairs, with the treatment subsequently randomly assigned within each pair. This use of matching is related but distinct.

Nearest-neighbor matching

Nearest-neighbor matching (NNM) uses distance between covariate patterns to define “closest”. There are many ways to define the distance between two covariate patterns. We could use squared differences as a distance measure, but this measure ignores problems with scale and covariance. Weighting the differences by the inverse of the sample covariance matrix handles these issues. Other measures are also used, but these details are less important than the costs and benefits of NNM dropping the functional-form assumptions (linear, logit, probit, etc.) used in the estimators discussed last time.

Dropping the functional-form assumptions makes the NNM estimator much more flexible; it estimates the ATE for a much wider class of models. The cost of this flexibility is that the NNM estimator requires much more data and the amount of data it needs grows with each additional continuous covariate.

In the previous blog entry, we used an example of mother’s smoking status on birthweight. Let’s reconsider that example.

. webuse cattaneo2.dta, clear


Now, we use teffects nnmatch to estimate the ATE by NNM.

. teffects nnmatch (bweight mmarried mage fage medu prenatal1) (mbsmoke)

Treatment-effects estimation                    Number of obs      =      4642
Estimator      : nearest-neighbor matching      Matches: requested =         1
Outcome model  : matching                                      min =         1
Distance metric: Mahalanobis                                   max =        16
------------------------------------------------------------------------------
|              AI Robust
bweight |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE          |
mbsmoke |
(smoker  |
vs  |
nonsmoker)  |  -210.5435   29.32969    -7.18   0.000    -268.0286   -153.0584
------------------------------------------------------------------------------


The estimated ATE is -211, meaning that infants would weigh 211 grams less when all mothers smoked than when no mothers smoked.

The output also indicates that ties in distance caused at least one observation to be matched with 16 other observations, even though we requested only matching. NNM averages the outcomes of all the tied-in-distance observations, as it should. (They are all equally good and using all of them will reduce bias.)

NNM on discrete covariates does not guarantee exact matching. For example, some married women could be matched with single women. We probably prefer exact matching on discrete covariates, which we do now.

. teffects nnmatch (bweight mmarried mage fage medu prenatal1) (mbsmoke), ///
ematch(mmarried prenatal1)

Treatment-effects estimation                    Number of obs      =      4642
Estimator      : nearest-neighbor matching      Matches: requested =         1
Outcome model  : matching                                      min =         1
Distance metric: Mahalanobis                                   max =        16
------------------------------------------------------------------------------
|              AI Robust
bweight |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE          |
mbsmoke |
(smoker  |
vs  |
nonsmoker)  |  -209.5726   29.32603    -7.15   0.000    -267.0506   -152.0946
------------------------------------------------------------------------------


Exact matching on mmarried and prenatal1 changed the results a little bit.

Using more than one continuous covariate introduces large-sample bias, and we have three. The option biasadj() uses a linear model to remove the large-sample bias, as suggested by Abadie and Imbens (2006, 2011).

. teffects nnmatch (bweight mmarried mage fage medu prenatal1) (mbsmoke), ///
ematch(mmarried prenatal1)  biasadj(mage fage medu)

Treatment-effects estimation                    Number of obs      =      4642
Estimator      : nearest-neighbor matching      Matches: requested =         1
Outcome model  : matching                                      min =         1
Distance metric: Mahalanobis                                   max =        16
------------------------------------------------------------------------------
|              AI Robust
bweight |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE          |
mbsmoke |
(smoker  |
vs  |
nonsmoker)  |  -210.0558   29.32803    -7.16   0.000    -267.5377   -152.5739
------------------------------------------------------------------------------


In this case, the results changed by a small amount. In general, they can change a lot, and the amount increases with the number of continuous
covariates.

Propensity-score matching

NNM uses bias adjustment to remove the bias caused by matching on more than one continuous covariate. The generality of this approach makes it very appealing, but it can be difficult to think about issues of fit and model specification. Propensity-score matching (PSM) matches on an estimated probability of treatment known as the propensity score. There is no need for bias adjustment because we match on only one continuous covariate. PSM has the added benefit that we can use all the standard methods for checking the fit of binary regression models prior to matching.

We estimate the ATE by PSM using teffects psmatch.

. teffects psmatch (bweight) (mbsmoke mmarried mage fage medu prenatal1 )

Treatment-effects estimation                    Number of obs      =      4642
Estimator      : propensity-score matching      Matches: requested =         1
Outcome model  : matching                                      min =         1
Treatment model: logit                                         max =        16
------------------------------------------------------------------------------
|              AI Robust
bweight |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE          |
mbsmoke |
(smoker  |
vs  |
nonsmoker)  |  -229.4492   25.88746    -8.86   0.000    -280.1877   -178.7107
------------------------------------------------------------------------------


The estimated ATE is now -229, larger in magnitude than the NNM estimates but not significantly so.

How to choose among the six estimators

We now have six estimators:

1. RA: Regression adjustment
2. IPW: Inverse probability weighting
3. IPWRA: Inverse probability weighting with regression adjustment
4. AIPW: Augmented inverse probability weighting
5. NNM: Nearest-neighbor matching
6. PSM: Propensity-score matching

The ATEs we estimated are

1. RA: -277.06
2. IPW: -275.56
3. IPWRA: -229.97
4. AIPW: -230.99
5. NNM: -210.06
6. PSM: -229.45

Which estimator should we use?

We would never suggest searching the above table for the result that most closely fits your wishes and biases. The choice of estimator needs to be made beforehand.

So, how do we choose?

Here are some rules of thumb:

1. Under correct specification, all the estimators should produce similar results. (Similar estimates do not guarantee correct specification because all the specifications could be wrong.)
2. When you know the determinants of treatment status, IPW is a natural base-case estimator.
3. When you instead know the determinants of the outcome, RA is a natural base-case estimator.
4. The doubly robust estimators, AIPW and IPWRA, give us an extra shot at correct specification.
5. When you have lots of continuous covariates, NNM will crucially hinge on the bias adjustment, and the computation gets to be extremely difficult.
6. When you know the determinants of treatment status, PSM is another base-case estimator.
7. The IPW estimators are not reliable when the estimated treatment probabilities get too close to 0 or 1.

Final thoughts

Before we go, we reiterate the cautionary note from our last entry. Nothing about the mathematics of treatment-effects estimators magically extracts causal relationships from observational data. We cannot thoughtlessly analyze our data using Stata’s teffects commands and infer a causal relationship. The models must be supported by scientific theory.

If you would like to learn more about treatment effects in Stata, there is an entire manual devoted to the treatment-effects features in Stata 14; it includes a basic introduction, an advanced introduction, and many worked examples. In Stata, type help teffects:

.  help teffects


Title

[TE] teffects—Treatment-effects estimation for observational data

Syntax

… <output omitted> …

The title [TE] teffects will be in blue, which means it’s clickable. Click on it to go to the Treatment-Effects Reference Manual.

Or download the manual from our website; visit

http://www.stata.com/manuals14/te/

References

Abadie, A., and Imbens, G. W. 2006. Large sample properties of matching estimators for average treatment effects. Econometrica 74: 235–267.

Abadie, A., and Imbens, G. W. 2011. Bias-corrected matching estimators for average treatment effects. Journal of Business and Economic Statistics 29: 1–11.

Cattaneo, M. D. 2010. Efficient semiparametric estimation of multi-valued treatment effects under ignorability. Journal of Econometrics 155: 138–154.

Categories: Statistics Tags:

## Introduction to treatment effects in Stata: Part 1

This post was written jointly with David Drukker, Director of Econometrics, StataCorp.

The topic for today is the treatment-effects features in Stata.

Treatment-effects estimators estimate the causal effect of a treatment on an outcome based on observational data.

In today’s posting, we will discuss four treatment-effects estimators:

1. RA: Regression adjustment
2. IPW: Inverse probability weighting
3. IPWRA: Inverse probability weighting with regression adjustment
4. AIPW: Augmented inverse probability weighting

We’ll save the matching estimators for part 2.

We should note that nothing about treatment-effects estimators magically extracts causal relationships. As with any regression analysis of observational data, the causal interpretation must be based on a reasonable underlying scientific rationale.

Introduction

We are going to discuss treatments and outcomes.

A treatment could be a new drug and the outcome blood pressure or cholesterol levels. A treatment could be a surgical procedure and the outcome patient mobility. A treatment could be a job training program and the outcome employment or wages. A treatment could even be an ad campaign designed to increase the sales of a product.

Consider whether a mother’s smoking affects the weight of her baby at birth. Questions like this one can only be answered using observational data. Experiments would be unethical.

The problem with observational data is that the subjects choose whether to get the treatment. For example, a mother decides to smoke or not to smoke. The subjects are said to have self-selected into the treated and untreated groups.

In an ideal world, we would design an experiment to test cause-and-effect and treatment-and-outcome relationships. We would randomly assign subjects to the treated or untreated groups. Randomly assigning the treatment guarantees that the treatment is independent of the outcome, which greatly simplifies the analysis.

Causal inference requires the estimation of the unconditional means of the outcomes for each treatment level. We only observe the outcome of each subject conditional on the received treatment regardless of whether the data are observational or experimental. For experimental data, random assignment of the treatment guarantees that the treatment is independent of the outcome; so averages of the outcomes conditional on observed treatment estimate the unconditional means of interest. For observational data, we model the treatment assignment process. If our model is correct, the treatment assignment process is considered as good as random conditional on the covariates in our model.

Let’s consider an example. Figure 1 is a scatterplot of observational data similar to those used by Cattaneo (2010). The treatment variable is the mother’s smoking status during pregnancy, and the outcome is the birthweight of her baby.

The red points represent the mothers who smoked during pregnancy, while the green points represent the mothers who did not. The mothers themselves chose whether to smoke, and that complicates the analysis.

We cannot estimate the effect of smoking on birthweight by comparing the mean birthweights of babies of mothers who did and did not smoke. Why not? Look again at our graph. Older mothers tend to have heavier babies regardless of whether they smoked while pregnant. In these data, older mothers were also more likely to be smokers. Thus, mother’s age is related to both treatment status and outcome. So how should we proceed?

RA: The regression adjustment estimator

RA estimators model the outcome to account for the nonrandom treatment assignment.

We might ask, “How would the outcomes have changed had the mothers who smoked chosen not to smoke?” or “How would the outcomes have changed had the mothers who didn’t smoke chosen to smoke?”. If we knew the answers to these counterfactual questions, analysis would be easy: we would just subtract the observed outcomes from the counterfactual outcomes.

The counterfactual outcomes are called unobserved potential outcomes in the treatment-effects literature. Sometimes the word unobserved is dropped.

We can construct measurements of these unobserved potential outcomes, and our data might look like this:

In figure 2, the observed data are shown using solid points and the unobserved potential outcomes are shown using hollow points. The hollow red points represent the potential outcomes for the smokers had they not smoked. The hollow green points represent the potential outcomes for the nonsmokers had they smoked.

We can estimate the unobserved potential outcomes then by fitting separate linear regression models with the observed data (solid points) to the two treatment groups.

In figure 3, we have one regression line for nonsmokers (the green line) and a separate regression line for smokers (the red line).

Let’s understand what the two lines mean:

The green point on the left in figure 4, labeled Observed, is an observation for a mother who did not smoke. The point labeled E(y0) on the green regression line is the expected birthweight of the baby given the mother’s age and that she didn’t smoke. The point labeled E(y1) on the red regression line is the expected birthweight of the baby for the same mother had she smoked.

The difference between these expectations estimates the covariate-specific treatment effect for those who did not get the treatment.

Now, let’s look at the other counterfactual question.

The red point on the right in figure 4, labeled Observed in red, is an observation for a mother who smoked during pregnancy. The points on the green and red regression lines again represent the expected birthweights — the potential outcomes — of the mother’s baby under the two treatment conditions.

The difference between these expectations estimates the covariate-specific treatment effect for those who got the treatment.

Note that we estimate an average treatment effect (ATE), conditional on covariate values, for each subject. Furthermore, we estimate this effect for each subject, regardless of which treatment was actually received. Averages of these effects over all the subjects in the data estimate the ATE.

We could also use figure 4 to motivate a prediction of the outcome that each subject would obtain for each treatment level, regardless of the treatment recieved. The story is analogous to the one above. Averages of these predictions over all the subjects in the data estimate the potential-outcome means (POMs) for each treatment level.

It is reassuring that differences in the estimated POMs is the same estimate of the ATE discussed above.

The ATE on the treated (ATET) is like the ATE, but it uses only the subjects who were observed in the treatment group. This approach to calculating treatment effects is called regression adjustment (RA).

Let’s open a dataset and try this using Stata.

. webuse cattaneo2.dta, clear
(Excerpt from Cattaneo (2010) Journal of Econometrics 155: 138-154)

To estimate the POMs in the two treatment groups, we type

. teffects ra (bweight mage) (mbsmoke), pomeans


We specify the outcome model in the first set of parentheses with the outcome variable followed by its covariates. In this example, the outcome variable is bweight and the only covariate is mage.

We specify the treatment model — simply the treatment variable — in the second set of parentheses. In this example, we specify only the treatment variable mbsmoke. We’ll talk about covariates in the next section.

The result of typing the command is

. teffects ra (bweight mage) (mbsmoke), pomeans

Iteration 0:   EE criterion =  7.878e-24
Iteration 1:   EE criterion =  8.468e-26

Treatment-effects estimation                    Number of obs      =      4642
Estimator      : regression adjustment
Outcome model  : linear
Treatment model: none
------------------------------------------------------------------------------
|               Robust
bweight |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
POmeans      |
mbsmoke |
nonsmoker  |   3409.435   9.294101   366.84   0.000     3391.219    3427.651
smoker  |   3132.374   20.61936   151.91   0.000     3091.961    3172.787
------------------------------------------------------------------------------


The output reports that the average birthweight would be 3,132 grams if all mothers smoked and 3,409 grams if no mother smoked.

We can estimate the ATE of smoking on birthweight by subtracting the POMs: 3132.374 – 3409.435 = -277.061. Or we can reissue our teffects ra command with the ate option and get standard errors and confidence intervals:

. teffects ra (bweight mage) (mbsmoke), ate

Iteration 0:   EE criterion =  7.878e-24
Iteration 1:   EE criterion =  5.185e-26

Treatment-effects estimation                    Number of obs      =      4642
Estimator      : regression adjustment
Outcome model  : linear
Treatment model: none
-------------------------------------------------------------------------------
|               Robust
bweight |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
ATE           |
mbsmoke |
(smoker vs    |
nonsmoker)  |  -277.0611   22.62844   -12.24   0.000    -321.4121   -232.7102
--------------+----------------------------------------------------------------
POmean        |
mbsmoke |
nonsmoker  |   3409.435   9.294101   366.84   0.000     3391.219    3427.651
-------------------------------------------------------------------------------


The output reports the same ATE we calculated by hand: -277.061. The ATE is the average of the differences between the birthweights when each mother smokes and the birthweights when no mother smokes.

We can also estimate the ATET by using the teffects ra command with option atet, but we will not do so here.

IPW: The inverse probability weighting estimator

RA estimators model the outcome to account for the nonrandom treatment assignment. Some researchers prefer to model the treatment assignment process and not specify a model for the outcome.

We know that smokers tend to be older than nonsmokers in our data. We also hypothesize that mother’s age directly affects birthweight. We observed this in figure 1, which we show again below.

This figure shows that treatment assignment depends on mother’s age. We would like to have a method of adjusting for this dependence. In particular, we wish we had more upper-age green points and lower-age red points. If we did, the mean birthweight for each group would change. We don’t know how that would affect the difference in means, but we do know it would be a better estimate of the difference.

To achieve a similar result, we are going to weight smokers in the lower-age range and nonsmokers in the upper-age range more heavily, and weight smokers in the upper-age range and nonsmokers in the lower-age range less heavily.

We will fit a probit or logit model of the form

Pr(woman smokes) = F(a + b*age)

teffects uses logit by default, but we will specify the probit option for illustration.

Once we have fit that model, we can obtain the prediction Pr(woman smokes) for each observation in the data; we’ll call this pi. Then, in making our POMs calculations — which is just a mean calculation — we will use those probabilities to weight the observations. We will weight observations on smokers by 1/pi so that weights will be large when the probability of being a smoker is small. We will weight observations on nonsmokers by 1/(1-pi) so that weights will be large when the probability of being a nonsmoker is small.

That results in the following graph replacing figure 1:

In figure 5, larger circles indicate larger weights.

To estimate the POMs with this IPW estimator, we can type

. teffects ipw (bweight) (mbsmoke mage, probit), pomeans


The first set of parentheses specifies the outcome model, which is simply the outcome variable in this case; there are no covariates. The second set of parentheses specifies the treatment model, which includes the outcome variable (mbsmoke) followed by covariates (in this case, just mage) and the kind of model (probit).

The result is

. teffects ipw (bweight) (mbsmoke mage, probit), pomeans

Iteration 0:   EE criterion =  3.615e-15
Iteration 1:   EE criterion =  4.381e-25

Treatment-effects estimation                    Number of obs      =      4642
Estimator      : inverse-probability weights
Outcome model  : weighted mean
Treatment model: probit
------------------------------------------------------------------------------
|               Robust
bweight |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
POmeans      |
mbsmoke |
nonsmoker  |   3408.979   9.307838   366.25   0.000     3390.736    3427.222
smoker  |   3133.479   20.66762   151.61   0.000     3092.971    3173.986
------------------------------------------------------------------------------


Our output reports that the average birthweight would be 3,133 grams if all the mothers smoked and 3,409 grams if none of the mothers smoked.

This time, the ATE is -275.5, and if we typed

. teffects ipw (bweight) (mbsmoke mage, probit), ate
(Output omitted)


we would learn that the standard error is 22.68 and the 95% confidence interval is [-319.9,231.0].

Just as with teffects ra, if we wanted ATET, we could specify the teffects ipw command with the atet option.

IPWRA: The IPW with regression adjustment estimator

RA estimators model the outcome to account for the nonrandom treatment assignment. IPW estimators model the treatment to account for the nonrandom treatment assignment. IPWRA estimators model both the outcome and the treatment to account for the nonrandom treatment assignment.

IPWRA uses IPW weights to estimate corrected regression coefficients that are subsequently used to perform regression adjustment.

The covariates in the outcome model and the treatment model do not have to be the same, and they often are not because the variables that influence a subject’s selection of treatment group are often different from the variables associated with the outcome. The IPWRA estimator has the double-robust property, which means that the estimates of the effects will be consistent if either the treatment model or the outcome model — but not both — are misspecified.

Let’s consider a situation with more complex outcome and treatment models but still using our low-birthweight data.

The outcome model will include

1. mage: the mother’s age
2. prenatal1: an indicator for prenatal visit during the first trimester
3. mmarried: an indicator for marital status of the mother
4. fbaby: an indicator for being first born

The treatment model will include

1. all the covariates of the outcome model
2. mage^2
3. medu: years of maternal education

We will also specify the aequations option to report the coefficients of the outcome and treatment models.

. teffects ipwra (bweight mage prenatal1 mmarried fbaby)                ///
(mbsmoke mmarried c.mage##c.mage fbaby medu, probit)   ///
, pomeans aequations

Iteration 0:   EE criterion =  1.001e-20
Iteration 1:   EE criterion =  1.134e-25

Treatment-effects estimation                    Number of obs      =      4642
Estimator      : IPW regression adjustment
Outcome model  : linear
Treatment model: probit
-------------------------------------------------------------------------------
|               Robust
bweight |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
POmeans       |
mbsmoke |
nonsmoker  |   3403.336    9.57126   355.58   0.000     3384.576    3422.095
smoker  |   3173.369   24.86997   127.60   0.000     3124.624    3222.113
--------------+----------------------------------------------------------------
OME0          |
mage |   2.893051   2.134788     1.36   0.175    -1.291056    7.077158
prenatal1 |   67.98549   28.78428     2.36   0.018     11.56933    124.4017
mmarried |   155.5893   26.46903     5.88   0.000      103.711    207.4677
fbaby |   -71.9215   20.39317    -3.53   0.000    -111.8914   -31.95162
_cons |   3194.808   55.04911    58.04   0.000     3086.913    3302.702
--------------+----------------------------------------------------------------
OME1          |
mage |  -5.068833   5.954425    -0.85   0.395    -16.73929    6.601626
prenatal1 |   34.76923   43.18534     0.81   0.421    -49.87248    119.4109
mmarried |   124.0941   40.29775     3.08   0.002     45.11193    203.0762
fbaby |   39.89692   56.82072     0.70   0.483    -71.46966    151.2635
_cons |   3175.551   153.8312    20.64   0.000     2874.047    3477.054
--------------+----------------------------------------------------------------
TME1          |
mmarried |  -.6484821   .0554173   -11.70   0.000     -.757098   -.5398663
mage |   .1744327   .0363718     4.80   0.000     .1031452    .2457202
|
c.mage#c.mage |  -.0032559   .0006678    -4.88   0.000    -.0045647   -.0019471
|
fbaby |  -.2175962   .0495604    -4.39   0.000    -.3147328   -.1204595
medu |  -.0863631   .0100148    -8.62   0.000    -.1059917   -.0667345
_cons |  -1.558255   .4639691    -3.36   0.001    -2.467618   -.6488926
-------------------------------------------------------------------------------


The POmeans section of the output displays the POMs for the two treatment groups. The ATE is now calculated to be 3173.369 – 3403.336 = -229.967.

The OME0 and OME1 sections display the RA coefficients for the untreated and treated groups, respectively.

The TME1 section of the output displays the coefficients for the probit treatment model.

Just as in the two previous cases, if we wanted the ATE with standard errors, etc., we would specify the ate option. If we wanted ATET, we would specify the atet option.

AIPW: The augmented IPW estimator

IPWRA estimators model both the outcome and the treatment to account for the nonrandom treatment assignment. So do AIPW estimators.

The AIPW estimator adds a bias-correction term to the IPW estimator. If the treatment model is correctly specified, the bias-correction term is 0 and the model is reduced to the IPW estimator. If the treatment model is misspecified but the outcome model is correctly specified, the bias-correction term corrects the estimator. Thus, the bias-correction term gives the AIPW estimator the same double-robust property as the IPWRA estimator.

The syntax and output for the AIPW estimator is almost identical to that for the IPWRA estimator.

. teffects aipw (bweight mage prenatal1 mmarried fbaby)                 ///
(mbsmoke mmarried c.mage##c.mage fbaby medu, probit)    ///
, pomeans aequations

Iteration 0:   EE criterion =  4.632e-21
Iteration 1:   EE criterion =  5.810e-26

Treatment-effects estimation                    Number of obs      =      4642
Estimator      : augmented IPW
Outcome model  : linear by ML
Treatment model: probit
-------------------------------------------------------------------------------
|               Robust
bweight |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
POmeans       |
mbsmoke |
nonsmoker  |   3403.355   9.568472   355.68   0.000     3384.601    3422.109
smoker  |   3172.366   24.42456   129.88   0.000     3124.495    3220.237
--------------+----------------------------------------------------------------
OME0          |
mage |   2.546828   2.084324     1.22   0.222    -1.538373    6.632028
prenatal1 |   64.40859   27.52699     2.34   0.019     10.45669    118.3605
mmarried |   160.9513    26.6162     6.05   0.000     108.7845    213.1181
fbaby |   -71.3286   19.64701    -3.63   0.000     -109.836   -32.82117
_cons |   3202.746   54.01082    59.30   0.000     3096.886    3308.605
--------------+----------------------------------------------------------------
OME1          |
mage |  -7.370881    4.21817    -1.75   0.081    -15.63834    .8965804
prenatal1 |   25.11133   40.37541     0.62   0.534    -54.02302    104.2457
mmarried |   133.6617   40.86443     3.27   0.001      53.5689    213.7545
fbaby |   41.43991   39.70712     1.04   0.297    -36.38461    119.2644
_cons |   3227.169   104.4059    30.91   0.000     3022.537    3431.801
--------------+----------------------------------------------------------------
TME1          |
mmarried |  -.6484821   .0554173   -11.70   0.000     -.757098   -.5398663
mage |   .1744327   .0363718     4.80   0.000     .1031452    .2457202
|
c.mage#c.mage |  -.0032559   .0006678    -4.88   0.000    -.0045647   -.0019471
|
fbaby |  -.2175962   .0495604    -4.39   0.000    -.3147328   -.1204595
medu |  -.0863631   .0100148    -8.62   0.000    -.1059917   -.0667345
_cons |  -1.558255   .4639691    -3.36   0.001    -2.467618   -.6488926
-------------------------------------------------------------------------------


The ATE is 3172.366 – 3403.355 = -230.989.

Final thoughts

The example above used a continuous outcome: birthweight. teffects can also be used with binary, count, and nonnegative continuous outcomes.

The estimators also allow multiple treatment categories.

An entire manual is devoted to the treatment-effects features in Stata 13, and it includes a basic introduction, advanced discussion, and worked examples. If you would like to learn more, you can download the [TE] Treatment-effects Reference Manual from the Stata website.

More to come

Next time, in part 2, we will cover the matching estimators.

Reference

Cattaneo, M. D. 2010. Efficient semiparametric estimation of multi-valued treatment effects under ignorability. Journal of Econometrics 155: 138–154.

## Using gmm to solve two-step estimation problems

Two-step estimation problems can be solved using the gmm command.

When a two-step estimator produces consistent point estimates but inconsistent standard errors, it is known as the two-step-estimation problem. For instance, inverse-probability weighted (IPW) estimators are a weighted average in which the weights are estimated in the first step. Two-step estimators use first-step estimates to estimate the parameters of interest in a second step. The two-step-estimation problem arises because the second step ignores the estimation error in the first step.

One solution is to convert the two-step estimator into a one-step estimator. My favorite way to do this conversion is to stack the equations solved by each of the two estimators and solve them jointly. This one-step approach produces consistent point estimates and consistent standard errors. There is no two-step problem because all the computations are performed jointly. Newey (1984) derives and justifies this approach.

I’m going to illustrate this approach with the IPW example, but it can be used with any two-step problem as long as each step is continuous.

IPW estimators are frequently used to estimate the mean that would be observed if everyone in a population received a specified treatment, a quantity known as a potential-outcome mean (POM). A difference of POMs is called the average treatment effect (ATE). Aside from all that, it is the mechanics of the two-step IPW estimator that interest me here. IPW estimators are weighted averages of the outcome, and the weights are estimated in a first step. The weights used in the second step are the inverse of the estimated probability of treatment.

Let’s imagine we are analyzing an extract of the birthweight data used by Cattaneo (2010). In this dataset, bweight is the baby’s weight at birth, mbsmoke is 1 if the mother smoked while pregnant (and 0 otherwise), mmarried is 1 if the mother is married, and prenatal1 is 1 if the mother had a prenatal visit in the first trimester.

Let’s imagine we want to estimate the mean when all pregnant women smoked, which is to say, the POM for smoking. If we were doing substantive research, we would also estimate the POM when no pregnant women smoked. The difference between these estimated POMs would then estimate the ATE of smoking.

In the IPW estimator, we begin by estimating the probability weights for smoking. We fit a probit model of mbsmoke as a function of mmarried and prenatal1.

. use cattaneo2
(Excerpt from Cattaneo (2010) Journal of Econometrics 155: 138-154)

. probit mbsmoke mmarried prenatal1, vce(robust)

Iteration 0:   log pseudolikelihood = -2230.7484
Iteration 1:   log pseudolikelihood = -2102.6994
Iteration 2:   log pseudolikelihood = -2102.1437
Iteration 3:   log pseudolikelihood = -2102.1436

Probit regression                                 Number of obs   =       4642
Wald chi2(2)    =     259.42
Prob > chi2     =     0.0000
Log pseudolikelihood = -2102.1436                 Pseudo R2       =     0.0577

------------------------------------------------------------------------------
|               Robust
mbsmoke |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
mmarried |  -.6365472   .0478037   -13.32   0.000    -.7302407   -.5428537
prenatal1 |  -.2144569   .0547583    -3.92   0.000    -.3217811   -.1071327
_cons |  -.3226297   .0471906    -6.84   0.000    -.4151215   -.2301379
------------------------------------------------------------------------------


The results indicate that both mmarried and prenatal1 significantly predict whether the mother smoked while pregnant.

We want to calculate the inverse probabilities. We begin by getting the probabilities:

. predict double pr, pr


Now, we can obtain the inverse probabilities by typing

. generate double ipw = (mbsmoke==1)/pr


We can now perform the second step: calculate the mean for smokers by using the IPWs.

. mean bweight [pw=ipw]

Mean estimation                     Number of obs    =     864

--------------------------------------------------------------
|       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
bweight |   3162.868   21.71397      3120.249    3205.486
--------------------------------------------------------------
. mean bweight [pw=ipw] if mbsmoke


The point estimate reported by mean is consistent; the reported standard error Read more…

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## Stata 13 ships June 24

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