  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.
