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20242026
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stat.ML2026

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…

stat.ML2026

Semiparametric Efficient Test for Interpretable Distributional Treatment Effects

Houssam Zenati, Arthur Gretton

Distributional treatment effects can be invisible to means: a treatment may preserve average outcomes while changing tails, modes, dispersion, or rare-event probabilities. Kernel t…

stat.ML2026

Kernel Treatment Effects with Adaptively Collected Data

Houssam Zenati, Bariscan Bozkurt, Arthur Gretton

Adaptive experiments improve efficiency by adjusting treatment assignments based on past outcomes, but this adaptivity breaks the i.i.d.\ assumptions that underpin classical asympt…

stat.ML2026

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…

stat.ML2026

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…

stat.ML2026

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…