Causal reasoning is a crucial part of science and human intelligence. In order to discover
causal relationships from data, we need structure discovery methods. We provide a review …

Learning behavioral patterns from observational data has been a de-facto approach to
motion forecasting. Yet, the current paradigm suffers from two shortcomings: brittle under …

Local permutation tests for conditional independence Page 1 The Annals of Statistics 2022, Vol.
50, No. 6, 3388–3414 https://doi.org/10.1214/22-AOS2233 © Institute of Mathematical Statistics …

Distributional regression aims to estimate the full conditional distribution of a target variable,
given covariates. Popular methods include linear and tree ensemble based quantile …

Conditional independence (CI) testing is a fundamental and challenging task in modern
statistics and machine learning. Many modern methods for CI testing rely on powerful …

The Markov assumption (MA) is fundamental to the empirical validity of reinforcement
learning. In this paper, we propose a novel Forward-Backward Learning procedure to test …

Motivation Valid statistical inference is crucial for decision-making but difficult to obtain in
supervised learning with multimodal data, eg combinations of clinical features, genomic …

We introduce statistical testing under distributional shifts. We are interested in the hypothesis
P*∈ H 0 for a target distribution P*, but observe data from a different distribution Q*. We …

Reinforcement learning (RL) is a powerful technique that allows an autonomous agent to
learn an optimal policy to maximize the expected return. The optimality of various RL …

Conditional independence (CI) testing is a fundamental task in statistics and machine
learning, but its effectiveness is hindered by the challenges posed by high-dimensional …