Robust inference on the average treatment effect using the outcome highly adaptive lasso.

Area of Science:

• Statistics
• Biostatistics
• Econometrics

Background:

• Estimating average treatment effects often requires propensity score and outcome regression modeling.
• Flexible methods like machine learning can improve regression estimation.
• Optimal regression doesn't guarantee optimal treatment effect estimation, especially with instrumental variables.

Purpose of the Study:

• To propose a flexible penalized regression technique for propensity score estimation.
• To address limitations of the outcome-adaptive lasso, which is restricted to parametric models.
• To develop a novel estimator for improved average treatment effect estimation.

Main Methods:

• Introducing the outcome highly adaptive lasso (OHAL) estimator.
• Developing large sample theory for the OHAL estimator.
• Proposing closed-form confidence intervals for the OHAL estimator.

Main Results:

• The OHAL estimator offers greater flexibility than existing parametric methods.
• Simulations demonstrate the benefits of the proposed OHAL method over popular approaches.
• The method provides a robust way to estimate treatment effects in challenging settings.

Conclusions:

• The outcome highly adaptive lasso provides a more flexible and effective approach to estimating average treatment effects.
• This method enhances the accuracy of treatment effect estimation, particularly in the presence of instrumental variables.
• The proposed technique offers a valuable advancement for statistical modeling in causal inference.