8 citations · 18 across the 5 of their papers we have counts for
5 papers
Aggregate Bayesian Causal Forests: The ABCs of Flexible Causal Inference for Hierarchically Structured Data
Dan R. C. Thal, Lauren V. Forrow, Erin R. Lipman +2
This paper introduces aggregate Bayesian Causal Forests (aBCF), a new Bayesian model for causal inference using aggregated data. Aggregated data are common in policy evaluations wh…
VCBART: Bayesian trees for varying coefficients
Sameer K. Deshpande, Ray Bai, Cecilia Balocchi +2
The linear varying coefficient models posits a linear relationship between an outcome and covariates in which the covariate effects are modeled as functions of additional effect mo…
Monotone function estimation in the presence of extreme data coarsening: Analysis of preeclampsia and birth weight in urban Uganda
Jennifer E. Starling, Catherine E. Aiken, Jared S. Murray +2
This paper proposes a Bayesian hierarchical model to characterize the relationship between birth weight and maternal pre-eclampsia across gestation at a large maternity hospital in…
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…
BART with Targeted Smoothing: An analysis of patient-specific stillbirth risk
Jennifer E. Starling, Jared S. Murray, Carlos M. Carvalho +2
This article introduces BART with Targeted Smoothing, or tsBART, a new Bayesian tree-based model for nonparametric regression. The goal of tsBART is to introduce smoothness over a…