Double Robust, Flexible Adjustment Methods for Causal Inference: An Overview and an Evaluation

Abstract

Double robust methods for flexible covariate adjustment in causal inference have proliferated in recent years. Despite their apparent advantages, these methods remain underutilized by social scientists. It is also unclear whether these methods actually outperform more traditional methods in finite samples. This paper has two aims: It is a guide to some of the latest methods in double robust, flexible covariate adjustment for causal inference, and it compares these methods to more traditional statistical methods. It does this by using both simulated data where the treatment effect estimate is known, and then using comparisons of experimental and observational data from the National Supported Work Demonstration. Methods covered include Augmented Inverse Propensity Weighting, Targeted Maximum Likelihood Estimation, and Double/Debiased Machine Learning. Results suggest that these methods do not necessarily outperform OLS regression or matching on propensity score estimated by logistic regression, even in cases where the data generating process is not linear.