Doubly Robust Uniform Confidence Band for the Conditional Average Treatment Effect Function

TL;DR

This paper introduces a doubly robust method to estimate and visualize the heterogeneity of the average treatment effect across covariates, effectively handling high-dimensional data and providing an easy-to-compute confidence band.

Contribution

It develops a novel doubly robust estimator for the conditional average treatment effect that avoids the curse of dimensionality and includes a uniform confidence band for inference.

Findings

The estimator performs well in Monte Carlo simulations.

The method effectively captures heterogeneity in treatment effects.

Application to smoking and birth weights demonstrates practical utility.

Abstract

In this paper, we propose a doubly robust method to present the heterogeneity of the average treatment effect with respect to observed covariates of interest. We consider a situation where a large number of covariates are needed for identifying the average treatment effect but the covariates of interest for analyzing heterogeneity are of much lower dimension. Our proposed estimator is doubly robust and avoids the curse of dimensionality. We propose a uniform confidence band that is easy to compute, and we illustrate its usefulness via Monte Carlo experiments and an application to the effects of smoking on birth weights.