6 citations · 6 across the 4 of their papers we have counts for
23 papers
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…
Demonstration Experiments
Guido Imbens, Lorenzo Masoero, Alexander Rakhlin +2
Adaptive experiments are used extensively in online platforms, healthcare and biotechnology, and the social sciences. Often, the primary goal is not to precisely estimate a treatme…
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…
Causal clustering: design of cluster experiments under network interference
Davide Viviano, Lihua Lei, Guido Imbens +3
This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of network spillovers. We provide a framework to choose the clustering…
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…
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…