This paper aims to give a broad coverage of central concepts and principles involved in
automated causal inference and emerging approaches to causal discovery from iid data and …

The authors outline a cognitive and computational account of causal learning in children.
They propose that children use specialized cognitive systems that allow them to recover an …

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

It is commonplace to encounter heterogeneous or nonstationary data, of which the
underlying generating process changes across domains or over time. Such a distribution …

A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …

Causal modelling provides a powerful set of tools for identifying causal structure from
observed correlations. It is well known that such techniques fail for quantum systems, unless …

Probabilistic graphical models and decision graphs are powerful modeling tools for
reasoning and decision making under uncertainty. As modeling languages they allow a …

Bayesian networks are graphical structures for representing the probabilistic relationships
among a large number of variables and doing probabilistic inference with those variables …

The pcalg package for R can be used for the following two purposes: Causal structure
learning and estimation of causal effects from observational data. In this document, we give …

We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-
Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and …