602 citations · 2.9k across the 103 of their papers we have counts for
26 papers · 1 filter
Practical Kernel Tests of Conditional Independence
Roman Pogodin, Antonin Schrab, Yazhe Li +2
We describe a data-efficient, kernel-based approach to statistical testing of conditional independence. A major challenge of conditional independence testing is to obtain the corre…
Deep Hypothesis Tests Detect Clinically Relevant Subgroup Shifts in Medical Images
Lisa M. Koch, Christian M. Schürch, Christian F. Baumgartner +2
Distribution shifts remain a fundamental problem for the safe application of machine learning systems. If undetected, they may impact the real-world performance of such systems or…
Efficient Conditionally Invariant Representation Learning
Roman Pogodin, Namrata Deka, Yazhe Li +3
We introduce the Conditional Independence Regression CovariancE (CIRCE), a measure of conditional independence for multivariate continuous-valued variables. CIRCE applies as a regu…
A Neural Mean Embedding Approach for Back-door and Front-door Adjustment
Liyuan Xu, Arthur Gretton
We consider the estimation of average and counterfactual treatment effects, under two settings: back-door adjustment and front-door adjustment. The goal in both cases is to recover…
Maximum Likelihood Learning of Unnormalized Models for Simulation-Based Inference
Pierre Glaser, Michael Arbel, Samo Hromadka +2
We introduce two synthetic likelihood methods for Simulation-Based Inference (SBI), to conduct either amortized or targeted inference from experimental observations when a high-fid…
Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach
Yuchen Zhu, Limor Gultchin, Arthur Gretton +2
We propose a kernel-based nonparametric estimator for the causal effect when the cause is corrupted by error. We do so by generalizing estimation in the instrumental variable setti…