This paper develops a semi-parametric Bayesian regression model for estimating heterogeneous treatment effects from observational data. Standard nonlinear regression models, which may work quite well for prediction, can yield badly biased estimates of treatment effects when fit to data with strong confounding. Our Bayesian causal forests model avoids this problem by directly incorporating an es...

In this paper we provide Bayesian matching methods for finding the causal effect of a binary intake variable x ∈ {0, 1} on an outcome of interest y. One technique we introduce is a Bayesian variant of the classic Rosenbaum and Rubin (1983, 1984) propensity score matching method. We show how it is possible to find the posterior distribution of the Bayesian matched sample average treatment effect...

Phase I trials aim to establish appropriate clinical and statistical parameters to guide future clinical trials. With individual trials typically underpowered, systematic reviews and meta-analysis are desired to assess the totality of evidence. A high percentage of zero or missing outcomes often complicate such efforts. We use a systematic review of pediatric phase I oncology trials as an examp...

To provide insight into patient-level disease dynamics from data collected at irregular time intervals, this work extends applications of semi-parametric clustering for temporal mining. In the semi-parametric clustering framework, Markovian models provide useful parametric assumptions for modeling temporal dynamics, and a non-parametric method is used to cluster the temporal abstractions instea...