Flexible modeling of how an entire distribution changes with covariates is an important yet
challenging generalization of mean-based regression that has seen growing interest over …

We study causal representation learning, the task of inferring latent causal variables and
their causal relations from high-dimensional functions (“mixtures”) of the variables. Prior …

Abstract Machine learning approaches commonly rely on the assumption of independent
and identically distributed (iid) data. In reality, however, this assumption is almost always …

A fundamental problem in many sciences is the learning of causal structure underlying a
system, typically through observation and experimentation. Commonly, one even collects …

Being able to provide explanations for a model's decision has become a central requirement
for the development, deployment, and adoption of machine learning models. However, we …

Causal effects are usually studied in terms of the means of counterfactual distributions,
which may be insufficient in many scenarios. Given a class of densities known up to …

The average treatment effect, which is the difference in expectation of the counterfactuals, is
probably the most popular target effect in causal inference with binary treatments. However …

Causal discovery methods commonly assume that all data is independently and identically
distributed (iid) and that there are no unmeasured confounding variables. In practice, neither …

The Distributional Random Forest (DRF) is a recently introduced Random Forest algorithm
to estimate multivariate conditional distributions. Due to its general estimation procedure, it …

Structural causal models (SCMs) are widely used in various disciplines to represent causal
relationships among variables in complex systems. Unfortunately, the underlying causal …