Causal identification is at the core of the causal inference literature, where complete
algorithms have been proposed to identify causal queries of interest. The validity of these …

Probabilistic and Causal Inference: The Works of Judea Pearl: An Interventionist Approach to
Mediation Analysis Page 1 38 Judea Pearl’s insight that, when errors are assumed …

Drawbacks of ignoring the causal mechanisms when performing imitation learning have
recently been acknowledged. Several approaches both to assess the feasibility of imitation …

The aim of this paper is to discuss a recent result which shows that probabilistic inference in
the presence of (unknown) causal mechanisms can be tractable for models that have …

Conditional independence provides a way to understand causal relationships among the
variables of interest. An underlying system may exhibit more fine-grained causal …

We study the problem of learning" good" interventions in a stochastic environment modeled
by its underlying causal graph. Good interventions refer to interventions that maximize …

Causality has been a burgeoning field of research leading to the point where the literature
abounds with different components addressing distinct parts of causality. For researchers, it …

For the last three decades, Judea Pearl has been a leading advocate for the adoption of
causal models throughout the sciences. Pearl [1995] introduced causal models based on …

Causal inference in a sub-population involves identifying the causal effect of an intervention
on a specific subgroup, which is distinguished from the whole population through the …

Bayesian networks are widely used to learn and reason about the dependence structure of
discrete variables. However, they can only formally encode symmetric conditional …