Abstract Bayesian Networks (BNs) have become increasingly popular over the last few
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …

Causal machine learning (CML) has experienced increasing popularity in healthcare.
Beyond the inherent capabilities of adding domain knowledge into learning systems, CML …

We introduce in this survey the major concepts, models, and algorithms proposed so far to
infer causal relations from observational time series, a task usually referred to as causal …

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 …

Simulated DAG models may exhibit properties that, perhaps inadvertently, render their
structure identifiable and unexpectedly affect structure learning algorithms. Here, we show …

The free energy principle, an influential framework in computational neuroscience and
theoretical neurobiology, starts from the assumption that living systems ensure adaptive …

Understanding the laws that govern a phenomenon is the core of scientific progress. This is
especially true when the goal is to model the interplay between different aspects in a causal …

We study the class of location-scale or heteroscedastic noise models (LSNMs), in which the
effect $ Y $ can be written as a function of the cause $ X $ and a noise source $ N …

Learning behavioral patterns from observational data has been a de-facto approach to
motion forecasting. Yet, the current paradigm suffers from two shortcomings: brittle under …

Abstract Recovering underlying Directed Acyclic Graph (DAG) structures from observational
data is highly challenging due to the combinatorial nature of the DAG-constrained …