In many fields of scientific research and real-world applications, unbiased estimation of
causal effects from non-experimental data is crucial for understanding the mechanism …

Estimating causality from observational data is essential in many data science questions but
can be a challenging task. Here we review approaches to causality that are popular in …

We address the consistency of a kernel ridge regression estimate of the conditional mean
embedding (CME), which is an embedding of the conditional distribution of $ Y $ given $ X …

Conditional stochastic optimization has found applications in a wide range of machine
learning tasks, such as invariant learning, AUPRC maximization, and meta-learning. As the …

Non-linear Mendelian randomization is an extension to standard Mendelian randomization
to explore the shape of the causal relationship between an exposure and outcome using an …

We address the problem of causal effect estima-tion in the presence of unobserved
confounding, but where proxies for the latent confounder (s) areobserved. We propose two …

We develop an approach for estimating models described via conditional moment
restrictions, with a prototypical application being non-parametric instrumental variable …

Federated Learning (FL) enables a group of clients to jointly train a machine learning model
with the help of a centralized server. Clients do not need to submit their local data to the …

Causal inference from observational data is crucial for many disciplines such as medicine
and economics. However, sharp bounds for causal effects under relaxations of the …

We study the estimation of causal parameters when not all confounders are observed and
instead negative controls are available. Recent work has shown how these can enable …