Causal Forest and Doubly Robust Machine Learning for Political Science

Machine learning (ML) methods in general and tree-based methods in particular are on the rise in the social sciences. While ML is widely valued for predictive accuracy, new causally-oriented machine learning methods actually leverage this power for statistical inference and treatment effect estimation. In this article, we synthesize this causal ML literature into a new paradigm we coin "Doubly Robust Machine Learning," or DRML. Our conceptual framework makes clear the benefits of these new methods, like causal forest, and explains how researchers can fruitfully employ these approaches to improve on the power, precision, and coverage of standard estimators. We demonstrate these benefits for average and conditional effect estimation through a massive array of over one million Monte Carlo simulations spanning a wide range of possible research settings. Taken together, our results provide the conceptual and empirical basis for the use of DRML in social science applications.