Mediation Analysis in Causal Inference

Investigations in estimating causal effects defined in the presence of mediation (e.g., natural direct effect), based on stochastic treatment regimes.

Causal Inference with Stochastic Interventions

Investigations in causal inference with stochastic treatment regimes, a flexible formalism more realistic than dynamic or deterministic treatment rules.


The things that keep me from working.

R Packages

Software packages developed to extend the R programming language.


Brief adverts on recent teaching.

The PhD Years

Assorted notes on graduate school…


Records of recent professional travel.

Variance Shrinkage for Locally Efficient Estimators

Investigations in applying methods for variance moderation to stabilize locally efficient estimators for data analytic use in high-dimensional biology.

Data-Adaptive Differential Methylation Analysis

Investigations in the use of causal inference and ensemble machine learning to identify differentially methylated positions and regions.

Recent Publications

(see CV for a full list)

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Mediation analysis in causal inference has traditionally focused on binary treatment regimes and deterministic interventions, as well …

We focus on variable importance analysis in high-dimensional biological data sets with modest sample sizes, using semiparametric …

Arsenic exposure is a worldwide health concern associated with an increased risk of skin, lung, and bladder cancer but arsenic trioxide …

adaptest is an R package for performing multiple hypothesis testing in problem settings commonly encountered in high-dimensional …

Recent & Upcoming Talks

Much of the focus of statistical causal inference has been devoted to assessing the effects of static interventions, which specify a …

We consider nonparametrically estimating a parameter of interest under the constraint that a functional of the parameter is bounded. We …

DNA methylation is amongst the best studied of epigenetic mechanisms impacting gene expression. While much attention has been paid to …

Perhaps too often, work in statistical causal inference focuses on the effect of deterministic interventions, under which, for each …