activity
20172020
most citedRobust and Flexible Estimation of Stochastic Mediation Effects: A Proposed Method and Example in a Randomized Trial Setting

16 citations · 56 across the 8 of their papers we have counts for

collaborators

9 papers

stat.AP2020

tmleCommunity: A R Package Implementing Target Maximum Likelihood Estimation for Community-level Data

Chi Zhang, Jennifer Ahern, Mark J. van der Laan +1

Over the past years, many applications aim to assess the causal effect of treatments assigned at the community level, while data are still collected at the individual level among i…

stat.ML2020★ 2 cited

Adversarial Monte Carlo Meta-Learning of Optimal Prediction Procedures

Alex Luedtke, Incheoul Chung, Oleg Sofrygin

We frame the meta-learning of prediction procedures as a search for an optimal strategy in a two-player game. In this game, Nature selects a prior over distributions that generate…

stat.ME2018★ 7 cited

Complier stochastic direct effects: identification and robust estimation

Kara E Rudolph, Oleg Sofrygin, Mark J van der Laan

Mediation analysis is critical to understanding the mechanisms underlying exposure-outcome relationships. In this paper, we identify the instrumental variable (IV)-direct effect of…

stat.ME2018★ 7 cited

Evaluation of adaptive treatment strategies in an observational study where time-varying covariates are not monitored systematically

Noémi Kreif, Oleg Sofrygin, Julie Schmittdiel +5

In studies based on electronic health records (EHR), the frequency of covariate monitoring can vary by covariate type, across patients, and over time. This can lead to major challe…

stat.AP2017★ 16 cited

Robust and Flexible Estimation of Stochastic Mediation Effects: A Proposed Method and Example in a Randomized Trial Setting

Kara E. Rudolph, Oleg Sofrygin, Wenjing Zheng +1

Causal mediation analysis can improve understanding of the mechanisms underlying epidemiologic associations. However, the utility of natural direct and indirect effect estimation h…

stat.CO2017★ 3 cited

Conducting Simulations in Causal Inference with Networks-Based Structural Equation Models

Oleg Sofrygin, Romain Neugebauer, Mark J. van der Laan

The past decade has seen an increasing body of literature devoted to the estimation of causal effects in network-dependent data. However, the validity of many classical statistical…