The trustworthiness of machine learning has emerged as a critical topic in the field,
encompassing various applications and research areas such as robustness, security …

The premise of identifiable and causal representation learning is to improve the current
representation learning paradigm in terms of generalizability or robustness. Despite recent …

Scientific hypotheses typically concern specific aspects of complex, imperfectly understood
or entirely unknown mechanisms, such as the effect of gene expression levels on …

Instrumental variable (IV) regression relies on instruments to infer causal effects from
observational data with unobserved confounding. We consider IV regression in time series …

Causal inference is the process of using assumptions, study designs, and estimation
strategies to draw conclusions about the causal relationships between variables based on …

Contextual bandit and reinforcement learning algorithms have been successfully used in
various interactive learning systems such as online advertising, recommender systems, and …

In safety-critical applications, machine learning models should generalize well under worst-
case distribution shifts, that is, have a small robust risk. Invariance-based algorithms can …

Instrumental variable (IV) methods are used to estimate causal effects in settings with
unobserved confounding, where we cannot directly experiment on the treatment variable …

Linear non-Gaussian causal models postulate that each random variable is a linear function
of parent variables and non-Gaussian exogenous error terms. We study identification of the …

Scientists often want to learn about cause and effect from hierarchical data, collected from
subunits nested inside units. Consider students in schools, cells in patients, or cities in …