Causal Inference with Stochastic Interventions

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


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Software packages developed to extend the R programming language.


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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

The advent and subsequent widespread availability of preventive vaccines has altered the course of public health in the twentieth century. In spite of the overall success, vaccines are still lacking for many high-burden diseases, including HIV. An important step in the process of developing effective vaccines is identifying immune responses that are indicative of protective efficacy. In this work, we use a causal inference framework to propose a new approach to studying immune responses in the context of vaccines. We focus on causal quantities defined by stochastic interventions, which may be more relevant than alternative approaches for describing the effects of immune responses on risk of infection or disease. We propose methodology for efficiently estimating these quantities using data generated by preventive vaccine trials with two-phase sampling of immune responses. We propose and evaluate two strategies for estimating these quantities: an inverse probability weighting-based method and an augmented method. The latter method is shown to be nonparametric efficient and multiply robust to misspecification of nuisance estimators. We also provide methods for constructing confidence intervals and hypothesis tests, and provide an open source software implementation of the proposed methodology. We illustrate the methods using data from a recent preventive HIV vaccine trial.

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.

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