5 papers
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…
Adaptive Off-Policy Inference for M-Estimators Under Model Misspecification
James Leiner, Robin Dunn, Aaditya Ramdas
When data are collected adaptively, such as in bandit algorithms, classical statistical approaches such as ordinary least squares and -estimation will often fail to achieve asym…
Graph fission and cross-validation
James Leiner, Aaditya Ramdas
We introduce a technique called graph fission which takes in a graph which potentially contains only one observation per node (whose distribution lies in a known class) and produce…
Scalable Causal Structure Learning via Amortized Conditional Independence Testing
James Leiner, Brian Manzo, Aaditya Ramdas +1
Controlling false positives (Type I errors) through statistical hypothesis testing is a foundation of modern scientific data analysis. Existing causal structure discovery algorithm…
Data fission: splitting a single data point
James Leiner, Boyan Duan, Larry Wasserman +1
Suppose we observe a random vector from some distribution in a known family with unknown parameters. We ask the following question: when is it possible to split into tw…