flexBCF

Faster and more flexible implementation of Bayesian Causal Forests

This implementation was created as part of the 2022 American Causal Inference Competition (ACIC) Data Challenge. Please see our pre-print for more details.

Note that flexBCF implements a slightly different model than what is provided by default in the original bcf package (available at this link). Namely, it (i) does not use half-Normal or half-Cauchy priors for scale parameters; (ii) is not invariance to re-coding of the treatment indicator; and (iii) uses the same regression tree prior $\mu$ and $\tau$ (in contrast to bcf which more strongly regularizes $\tau$-trees towards shallow trees). If you require such functionality, please contact the package maintainer.

You can install flexBCF using

devtools::install_github(repo = "skdeshpande91/flexBCF", subdir = "flexBCF")