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

TL;DR

This paper discusses the Bayesian Causal Forest (BCF) model's performance and utility in causal inference, comparing it with Bayesian propensity scores and exploring its role in outcome modeling, highlighting future research directions.

Contribution

It provides an in-depth evaluation of the BCF model, clarifies its differences from Bayesian propensity scores, and discusses its potential in big data causal inference.

Findings

BCF model effectively estimates causal effects in various settings.

Differences between PS in BCF and Bayesian PS are clarified.

Future research avenues for BCF in big data are proposed.

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.