Fast & Flexible Implementation of Bayesian Causal Forests

A faster implementation of Bayesian Causal Forests (BCF; Hahn et al. (2020) ), which uses regression tree ensembles to estimate the conditional average treatment effect of a binary treatment on a scalar output as a function of many covariates. This implementation avoids many redundant computations and memory allocations present in the original BCF implementation, allowing the model to be fit to larger datasets. The implementation was originally developed for the 2022 American Causal Inference Conference's Data Challenge. See Kokandakar et al. (2023) for more details.