most citedTargeted Smooth Bayesian Causal Forests: An analysis of heterogeneous treatment effects for simultaneous versus interval medical abortion regimens over gestation

8 citations · 8 across the 1 of their papers we have counts for

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1 paper

stat.AP2019★ 8 cited

Targeted Smooth Bayesian Causal Forests: An analysis of heterogeneous treatment effects for simultaneous versus interval medical abortion regimens over gestation

Jennifer E. Starling, Jared S. Murray, Patricia A. Lohr +3

We introduce Targeted Smooth Bayesian Causal Forests (tsBCF), a nonparametric Bayesian approach for estimating heterogeneous treatment effects which vary smoothly over a single cov…