Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion)
By Liangyuan Hu. Bayesian Analysis, 2020
321 citations recorded by the source corpus.
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.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/18342afa1404e66a16fd04e53dcfd8337587fc20
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In Statistics and Probability
- Stefan Wager
- Xinkun Nie
- Susan Athey
- Edward H. Kennedy
- Mihaela van der Schaar
- Liangyuan Hu
- Guido W. Imbens
- José R. Zubizarreta
Citations in the corpus
- Quasi-oracle estimation of heterogeneous treatment effects (2017, background): “In particular, several recent papers have proposed structural changes to various machine learning methods aimed at focusing on accurate estimation of τ(·) (Athey and Imbens, 2016; Hahn et al., 2017; Imai and Ratkovic, 2013; Powers et al., 2018; Shalit et al., 2017; Su et al., 2009; Wager and Athey, ”