19 citations · 100 across the 32 of their papers we have counts for
36 papers
Policy Learning with Weak Signals
Benedikt Koch, Winston Chou, Aurélien Bibaut +1
Policy learning in digital experimentation faces three challenges: weak signal-to-noise ratios, rich covariate spaces, and massive data volumes. We formalize this regime by modelin…
Scalable Multi-Task Inverse Reinforcement Learning
Allen Tran, Jia Wan, Nathan Kallus +1
By learning transferable rewards, inverse reinforcement learning (IRL) enables counterfactual evaluation of agents under modified environments. Such transfer places strict requirem…
Adaptive inference for functionals of M-estimands
James Leiner, Aurélien Bibaut, Nathan Kallus +2
Reinforcement learning and contextual bandit algorithms have become increasingly common in sequential decision-making applications. When these methods are deployed in high-stakes d…
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…
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…
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…
Efficient Inference after Directionally Stable Adaptive Experiments
Zikai Shen, Houssam Zenati, Nathan Kallus +3
We study inference on scalar-valued pathwise differentiable targets after adaptive data collection, such as a bandit algorithm. We introduce a novel target-specific condition, dire…
Efficient Inference for Inverse Reinforcement Learning and Dynamic Discrete Choice Models
Lars van der Laan, Aurélien Bibaut, Nathan Kallus
In many sequential decision-making problems, researchers observe actions but not the rewards that drive behavior, yet still wish to evaluate and compare counterfactual policies. In…
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…
Fast Best-in-Class Regret for Contextual Bandits
Samuel Girard, Aurelien Bibaut, Arthur Gretton +2
We study the problem of stochastic contextual bandits in the agnostic setting, where the goal is to compete with the best policy in a given class without assuming realizability or…
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…
Simulation-Based Inference for Adaptive Experiments
Brian M Cho, Aurélien Bibaut, Nathan Kallus
Multi-arm bandit experimental designs are increasingly being adopted over standard randomized trials due to their potential to improve outcomes for study participants, enable faste…
Nonparametric Instrumental Variable Inference with Many Weak Instruments
Lars van der Laan, Nathan Kallus, Aurélien Bibaut
We study inference on linear functionals in the nonparametric instrumental variable (NPIV) problem with a discretely-valued instrument under a many-weak-instruments asymptotic regi…
Evaluating Decision Rules Across Many Weak Experiments
Winston Chou, Colin Gray, Nathan Kallus +2
Technology firms conduct randomized controlled experiments ("A/B tests") to learn which actions to take to improve business outcomes. In firms with mature experimentation platforms…
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands
Lars van der Laan, Aurelien Bibaut, Nathan Kallus +1
We develop a unified framework for automatic debiased machine learning (autoDML) for inference on a broad class of statistical parameters. The framework applies to any smooth funct…
Semiparametric Double Reinforcement Learning with Applications to Long-Term Causal Inference
Lars van der Laan, David Hubbard, Allen Tran +2
Double reinforcement learning (DRL) provides efficient off-policy inference for policy values in nonparametric Markov decision processes (MDPs), but fully nonparametric estimators…
Nonparametric Jackknife Instrumental Variable Estimation and Confounding Robust Surrogate Indices
Aurélien Bibaut, Nathan Kallus, Apoorva Lal
Jackknife instrumental variable estimation (JIVE) is a classic method to leverage many weak instrumental variables (IVs) to estimate linear structural models, overcoming the bias o…
Demistifying Inference after Adaptive Experiments
Aurélien Bibaut, Nathan Kallus
Adaptive experiments such as multi-arm bandits adapt the treatment-allocation policy and/or the decision to stop the experiment to the data observed so far. This has the potential…
Learning the Covariance of Treatment Effects Across Many Weak Experiments
Aurélien Bibaut, Winston Chou, Simon Ejdemyr +1
When primary objectives are insensitive or delayed, experimenters may instead focus on proxy metrics derived from secondary outcomes. For example, technology companies often infer…
Long-Term Causal Inference with Imperfect Surrogates using Many Weak Experiments, Proxies, and Cross-Fold Moments
Aurélien Bibaut, Nathan Kallus, Simon Ejdemyr +1
Inferring causal effects on long-term outcomes using short-term surrogates is crucial to rapid innovation. However, even when treatments are randomized and surrogates fully mediate…
Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments
Allen Tran, Aurélien Bibaut, Nathan Kallus
We study inference on the long-term causal effect of a continual exposure to a novel intervention, which we term a long-term treatment, based on an experiment involving only short-…
Near-Optimal Non-Parametric Sequential Tests and Confidence Sequences with Possibly Dependent Observations
Aurelien Bibaut, Nathan Kallus, Michael Lindon
Sequential tests and their implied confidence sequences, which are valid at arbitrary stopping times, promise flexible statistical inference and on-the-fly decision making. However…
One-step ahead sequential Super Learning from short times series of many slightly dependent data, and anticipating the cost of natural disasters
Geoffrey Ecoto, Aurélien Bibaut, Antoine Chambaz
Suppose that we observe a short time series where each time-t-specific data-structure consists of many slightly dependent data indexed by a and that we want to estimate a feature o…