25 citations · 71 across the 29 of their papers we have counts for
13 papers · 1 filter
Estimating Causal Effects from Data Generated by Stochastic Algorithms
Susan Athey, Guido Imbens, Zoe Ji
Recommendation systems and chatbots present content to users, typically using stochastic algorithms that select the content based on user characteristics or context. Examples of co…
Regression Adjustments for Double Randomization in Two-Sided Marketplaces
Timothy Sudijono, Lihua Lei, Lorenzo Masoero +3
Multiple randomization designs (MRDs) are a class of experimental designs used to handle interference in two-sided marketplaces. We investigate regression adjustment strategies for…
Power Analysis is Essential: High-Powered Tests Suggest Minimal to No Effect of Rounded Shapes on Click-Through Rates
Ron Kohavi, Jakub Linowski, Lukas Vermeer +5
Underpowered studies (below 50% power) suffer from the winner's curse: A statistically significant positive estimate must exaggerate the true treatment effect to meet the significa…
Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference
Carlos Cinelli, Avi Feller, Guido Imbens +3
Causality and causal inference have emerged as core research areas at the interface of modern statistics and domains including biomedical sciences, social sciences, computer scienc…
Triply Robust Panel Estimators
Susan Athey, Guido Imbens, Zhaonan Qu +1
This paper studies estimation of causal effects in a panel data setting. We introduce a new estimator, the Triply RObust Panel (TROP) estimator, that combines (i) a flexible model…
Causal Inference when Intervention Units and Outcome Units Differ
Georgia Papadogeorgou, Zhaoyan Song, Guido Imbens +1
We study causal inference in settings characterized by interference with a bipartite structure. There are two distinct sets of units: intervention units to which an intervention ca…