🇬🇧 Causal Machine Learning for Treatment Evaluation and Heterogeneous Treatment Effect Analysis

Abstract

This lecture provides an introduction to causal machine learning methods for estimating treatment effects in observational and experimental studies with high-dimensional covariates. The focus is on modern approaches that combine the flexibility of machine learning with causal inference in order to estimate treatment effects conditional on covariates and uncover effect heterogeneity across subgroups in a data-driven manner. The lecture discusses the challenges of treatment or impact evaluation when the set of potential confounders is large and conventional parametric models may be too restrictive. It also introduces two prominent approaches that allow researchers to control for covariates in a data-adaptive way and estimate heterogeneous treatment effects: causal (random) forests and the double machine learning framework, the latter of which can be combined with a wide range of machine learning methods. Applications from the health sciences/economics illustrate how these methods can be used to estimate both average and subgroup-specific causal effects and to detect effect heterogeneity. The lecture also demonstrates how these methods can be implemented in R, making the session useful for applied researchers interested in integrating causal machine learning into their empirical work.