14 papers
Semiparametric inference on identification sets in choice modeling
Antoine Scheid, Jia Wan, Guy Aridor +2
In a discrete choice model, choice probabilities observed for a finite collection of choice sets may not identify a counterfactual choice probability under an unobserved choice set…
The Value of Personalized Recommendations: Evidence from Netflix
Kevin Zielnicki, Guy Aridor, Aurélien Bibaut +3
Personalized recommendation systems shape much of user choice online, yet their targeted nature makes separating out the value of recommendation and the underlying goods challengin…
Reward Transfer from Inverse Reinforcement Learning: A Coupled Minimax Approach
Guang-Yuan Hao, Lars van der Laan, Aurélien Bibaut +1
We study the transfer of rewards learned using inverse reinforcement learning from expert demonstrations in one environment to reinforcement learning in a new, different environmen…
Nonparametric Instrumental Variable Analysis Without Structural Equations: Debiased Inference on Functionals of Inverse Problems with No Solutions
Zikai Shen, Nathan Kallus, Dimitri Meunier +3
We consider debiased inference on finite-dimensional functionals of infinite-dimensional least-squares solutions to inverse problems as a way to avoid having to assume exact soluti…
Inverse Reinforcement Learning with Just Classification and a Few Regressions
Lars van der Laan, Nathan Kallus, Aurelien Bibaut
Inverse reinforcement learning (IRL) aims to infer rewards from observed behavior, but rewards are not identified from the policy alone: many reward--value pairs can rationalize th…
Functional Natural Policy Gradients
Aurelien Bibaut, Houssam Zenati, Thibaud Rahier +1
We propose a cross-fitted debiasing device for policy learning from offline data. A key consequence of the resulting learning principle is regret even for policy classes…