Web: http://arxiv.org/abs/2209.06998

Sept. 16, 2022, 1:11 a.m. | Nikolay Krantsevich, Jingyu He, P. Richard Hahn

cs.LG updates on arXiv.org arxiv.org

Determining subgroups that respond especially well (or poorly) to specific
interventions (medical or policy) requires new supervised learning methods
tailored specifically for causal inference. Bayesian Causal Forest (BCF) is a
recent method that has been documented to perform well on data generating
processes with strong confounding of the sort that is plausible in many
applications. This paper develops a novel algorithm for fitting the BCF model,
which is more efficient than the previously available Gibbs sampler. The new
algorithm can …

arxiv effects stochastic tree

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