Heterogeneous treatment-effect estimation with S-, T-, and X-learners using H2OML
22 October 2025
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Motivation
In an era of large-scale experimentation and rich observational data, the one-size-fits-all paradigm is giving way to individualized decision-making. Whether targeting messages to voters, assigning medical treatments to patients, or recommending products to consumers, practitioners increasingly seek to tailor interventions based on individual characteristics. This shift hinges on understanding how treatment effects vary across individuals, not just whether interventions work on average, but for whom they work best. Read more…