Causal Inference with Stochastic Interventions

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


How I learned everything that I know.


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 thoughts on graduate school…


Records of recent professional travel.

Variance Moderation of 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 Identification of Differential Methylation

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

Selected Publications

methyvim is an R package providing facilities for nonparametric analysis of differential methylation using variable importance measures, a class of statistically estimable target parameters that arise in causal inference.
In F1000Research, 2018

We focus on variable importance analysis in high-dimensional biological data sets with modest sample sizes, using semiparametric statistical models. We present a method that is robust in small samples, but does not rely on arbitrary parametric assumptions, in the context of studies of gene expression and environmental exposures. Such analyses are faced not only with issues of multiple testing, but also the problem of teasing out the associations of biological expression measures with exposure, among numerous confounds such as age, race, and smoking. Specifically, we propose the use of targeted minimum loss-based estimation, coupled with generalizations of moderated empirical Bayes statistics, to obtain estimates of variable importance measures. The result is a data-adaptive approach that can estimate individual associations in high-dimensional data, even in the presence of relatively small samples.

origami is an R package that provides a general framework for the application of cross-validation schemes to particular functions. By allowing arbitrary lists of results, origami accommodates a range of cross-validation applications.
In JOSS, 2018

Recent Talks

(see CV for a full list)

More Talks

Towards the Realistic, Robust, and Efficient Assessment of Causal Effects with Stochastic Shift Interventions
Fri, Sep 14, 2018 10:00 AM
Fair Inference Through Semiparametric-Efficient Estimation Over Constraint-Specific Paths
Thu, Aug 2, 2018 8:00 AM
Data-Adaptive Estimation and Inference for Differential Methylation Analysis
Fri, Jul 27, 2018 11:15 AM
Robust Nonparametric Inference for Stochastic Interventions Under Multi-Stage Sampling
Mon, Apr 2, 2018 4:00 PM
Efficient Estimation of Survival Prognosis Under Immortal Time Bias
Mon, Mar 12, 2018 2:15 PM