5 papers
Evaluating for the long term: Learnings from industry
Leif Sigerson, Tom Cunningham, Winston Chou +22
Online platforms prioritize long-term business outcomes, yet typical experiments are far too short to measure these outcomes directly. Our goal in this paper is to collect and shar…
A Framework for Generalization and Transportation of Causal Estimates Under Covariate Shift
Apoorva Lal, Wenjing Zheng, Simon Ejdemyr
Randomized experiments are an excellent tool for estimating internally valid causal effects with the sample at hand, but their external validity is frequently debated. While classi…
A framework for causal segmentation analysis with machine learning in large-scale digital experiments
Nima S. Hejazi, Wenjing Zheng, Sathya Anand
We present an end-to-end methodological framework for causal segment discovery that aims to uncover differential impacts of treatments across subgroups of users in large-scale digi…
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…
A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure
Laura B. Balzer, Wenjing Zheng, Mark J. van der Laan +1
We often seek to estimate the impact of an exposure naturally occurring or randomly assigned at the cluster-level. For example, the literature on neighborhood determinants of healt…