Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …

A graphical model is a statistical model that is associated with a graph whose nodes
correspond to variables of interest. The edges of the graph reflect allowed conditional …

Causal disentanglement aims to uncover a representation of data using latent variables that
are interrelated through a causal model. Such a representation is identifiable if the latent …

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 …

A concise and self-contained introduction to causal inference, increasingly important in data
science and machine learning. The mathematization of causality is a relatively recent …

Causal network reconstruction from time series is an emerging topic in many fields of
science. Beyond inferring directionality between two time series, the goal of causal network …

In this essay I discuss potential outcome and graphical approaches to causality, and their
relevance for empirical work in economics. I review some of the work on directed acyclic …

We consider the problem of learning causal directed acyclic graphs from an observational
joint distribution. One can use these graphs to predict the outcome of interventional …

When domain knowledge is limited and experimentation is restricted by ethical, financial, or
time constraints, practitioners turn to observational causal discovery methods to recover the …

We consider structural equation models in which variables can be written as a function of
their parents and noise terms, which are assumed to be jointly independent. Corresponding …