Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalho

Abstract

Hahn et al. (2020) offers an extensive study to explicate and evaluate the performance of the BCF model in different settings and provides a detailed discussion about its utility in causal inference. It is a welcomed addition to the causal machine learning literature. I will emphasize the contribution of the BCF model to the field of causal inference through discussions on two topics: 1) the difference between the PS in the BCF model and the Bayesian PS in a Bayesian updating approach, 2) an alternative exposition of the role of the PS in outcome modeling based methods for the estimation of causal effects. I will conclude with comments on avenues for future research involving BCF that will be important and much needed in the era of Big data.

References in corpus (1)

• Gaussian Process Mixtures for Estimating Heterogeneous Treatment Effects

Cited by in corpus (4)

• A New Spatial Count Data Model with Bayesian Additive Regression Trees for Accident Hot Spot Identification
• On Inductive Biases for Heterogeneous Treatment Effect Estimation
• Causal Effect Inference for Structured Treatments
• Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators