Robust inference on the average treatment effect using the outcome highly adaptive lasso
This work addresses a methodological issue in causal inference for researchers, but it is incremental as it extends a previous parametric approach to a more flexible nonparametric framework.
The authors tackled the problem of estimating average treatment effects when using flexible regression techniques for propensity scores and outcome regressions, which can be suboptimal in the presence of strong instrumental variables. They proposed the outcome highly adaptive lasso, a nonparametric alternative to the outcome-adaptive lasso, and demonstrated via simulation that it offers benefits over existing methods, though no specific numerical results are provided.
Many estimators of the average effect of a treatment on an outcome require estimation of the propensity score, the outcome regression, or both. It is often beneficial to utilize flexible techniques such as semiparametric regression or machine learning to estimate these quantities. However, optimal estimation of these regressions does not necessarily lead to optimal estimation of the average treatment effect, particularly in settings with strong instrumental variables. A recent proposal addressed these issues via the outcome-adaptive lasso, a penalized regression technique for estimating the propensity score that seeks to minimize the impact of instrumental variables on treatment effect estimators. However, a notable limitation of this approach is that its application is restricted to parametric models. We propose a more flexible alternative that we call the outcome highly adaptive lasso. We discuss large sample theory for this estimator and propose closed form confidence intervals based on the proposed estimator. We show via simulation that our method offers benefits over several popular approaches.