J
Jean Pouget-Abadie
Researcher at Université de Montréal
Publications - 25
Citations - 43672
Jean Pouget-Abadie is an academic researcher from Université de Montréal. The author has contributed to research in topics: Graph (abstract data type) & Computer science. The author has an hindex of 10, co-authored 21 publications receiving 32708 citations. Previous affiliations of Jean Pouget-Abadie include Google & Harvard University.
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Proceedings Article
Cluster Randomized Designs for One-Sided Bipartite Experiments
TL;DR: In this paper , the authors formalize a natural model for interference in one-sided bipartite experiments using the exposure mapping framework and show that minimizing the bias of the difference-in-means estimator under their model results in a balanced partitioning clustering objective with a natural interpretation.
Optimizing Randomized and Deterministic Saturation Designs under Interference
TL;DR: In this article , the bias and variance of the difference-in-means estimator under a linear model of interference was investigated and a deterministic saturation design was proposed to further improve the estimator.
Proceedings ArticleDOI
Modeling Interference Using Experiment Roll-out
TL;DR: In this paper , the authors consider an experiment comparing two different policies for selecting the reserve price in a second-price advertising auction and show that the intervention can affect other advertisers through market competition, impacting the outcomes of even untreated advertisers.
Posted Content
Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls.
Nick Doudchenko,Khashayar Khosravi,Jean Pouget-Abadie,Sébastien Lahaie,Miles Lubin,Vahab Mirrokni,Jann Spiess,Guido W. Imbens +7 more
TL;DR: In this paper, the authors investigate the optimal design of experimental studies that have pre-treatment outcome data available and propose several methods for choosing the set of treated units in conjunction with the weights.
Proceedings ArticleDOI
Causal Estimation of User Learning in Personalized Systems
TL;DR: In this paper , a non-parametric causal model of user actions in a personalized recommendation system is introduced, and the authors derive new experimental designs that intervene in the personalization system to generate the variation necessary to separately identify the causal effect mediated through user learning and personalization.