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We introduce Targeted Smooth Bayesian Causal Forests (tsBCF), a nonparametricBayesian approach for estimating heterogeneous treatment effects which varysmoothly over a single covariate in the observational data setting. The tsBCFmethod induces smoothness by parameterizing terminal tree nodes with smoothfunctions, and allows for separate regularization of treatment effects versusprognostic effect of control covariates. Smoothing parameters for prognosticand treatment effects can be chosen to reflect prior knowledge or tuned in adata-dependent way.  We use tsBCF to analyze a new clinical protocol for early medical abortion.Our aim is to assess relative effectiveness of simultaneous versus intervaladministration of mifepristone and misoprostol over the first nine weeks ofgestation. The model reflects our expectation that the relative effectivenessvaries smoothly over gestation, but not necessarily over other covariates. Wedemonstrate the performance of the tsBCF method on benchmarking experiments.Software for tsBCF is available at https://github.com/jestarling/tsbcf/.

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