Treatment-effects estimation using lasso

Di Liu
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Di Liu: StataCorp

2022 Stata Conference from Stata Users Group

Abstract: You can use treatment-effects estimators to draw causal inferences from observational data. You can use lasso when you want to control for many potential covariates. With standard treatment-effects models, there is an intrinsic conflict between two required assumptions. The conditional independence assumption is likely to be satisfied with many variables in the model, while the overlap assumption is likely to be satisfied with fewer variables in the model. This presentation shows how to overcome this conflict by using Stata 17's telasso command. telasso estimates the average treatment effects with high-dimensional controls while using lasso for model selection. This estimator is robust to the model-selection mistakes. Moreover, it is doubly robust, so only one of the outcome or treatment model needs to be correctly specified.

Date: 2022-08-11
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Persistent link: https://EconPapers.repec.org/RePEc:boc:usug22:07

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