# Causal Inference using Bayesian Causal Forests

## Documentation for package ‘bcf’ version 2.0.2

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

bcf | Fit Bayesian Causal Forests
predict.bcf | Takes a fitted bcf object produced by bcf() along with serialized tree samples and produces predictions for a new set of covariate values
summary.bcf | Takes a fitted bcf object produced by bcf() and produces summary stats and MCMC diagnostics. This function is built using the coda package and meant to mimic output from rstan::print.stanfit(). It includes, for key parameters, posterior summary stats, effective sample sizes, and Gelman and Rubin's convergence diagnostics. By default, those parameters are: sigma (the error standard deviation when the weights are all equal), tau_bar (the estimated sample average treatment effect), mu_bar (the average outcome under control/z=0 across all observations in the sample), and yhat_bat (the average outcome under the realized treatment assignment across all observations in the sample).