I illustrate that exact matching on discrete covariates and regression adjustment (RA) with fully interacted discrete covariates perform the same nonparametric estimation. Read more…
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:
- RA: Regression adjustment
- IPW: Inverse probability weighting
- IPWRA: Inverse probability weighting with regression adjustment
- 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. Read more…