Double/Debiased Machine Learning for Treatment and Causal Parameters

TL;DR

This paper introduces a double/debiased machine learning approach that combines orthogonal scores and cross-fitting to accurately estimate causal parameters using flexible ML models, overcoming regularization bias.

Contribution

It develops a general framework for debiased causal inference with ML, enabling valid inference for treatment effects and other parameters using a broad set of ML tools.

Findings

Achieves root-n consistent and asymptotically normal estimators.

Provides valid confidence intervals for causal parameters.

Demonstrates effectiveness with various ML methods like random forests and neural networks.

Abstract

Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems very well. Achieving this goal does not imply that these methods automatically deliver good estimators of causal parameters. Examples of such parameters include individual regression coefficients, average treatment effects, average lifts, and demand or supply elasticities. In fact, estimates of such causal parameters obtained via naively plugging ML estimators into estimating equations for such parameters can behave very poorly due to the regularization bias. Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools. Specifically, we can form an orthogonal score for the target low-dimensional parameter by combining auxiliary and main ML predictions. The score is then used to build a de-biased estimator of the target parameter which typically will converge at the fastest possible 1/root(n) rate and be approximately unbiased and normal, and from which valid confidence intervals for these parameters of interest may be constructed. The resulting method thus could be called a "double ML" method because it relies on estimating primary and auxiliary predictive models. In order to avoid overfitting, our construction also makes use of the K-fold sample splitting, which we call cross-fitting. This allows us to use a very broad set of ML predictive methods in solving the auxiliary and main prediction problems, such as random forest, lasso, ridge, deep neural nets, boosted trees, as well as various hybrids and aggregators of these methods.