Most causal discovery algorithms in the literature exploit an assumption usually referred to as the Causal Faithfulness or Stability Condition. In this paper, we highlight two components of theâ€¦ (More)

This paper describes a new greedy Bayesian search algorithm (GBPS) and a new "combined" algorithm PC+GBPS for learning Bayesian networks. Simulation tests of these algorithms with previouslyâ€¦ (More)

We show that there is a general, informative and reliable procedure for discovering causal relations when, for all the investigator knows, both latent variables and selection bias may be at work.â€¦ (More)

The use of directed acyclic graphs (DAGs) to represent conditional independence relations among random variables has proved fruitful in a variety of ways. Recursive structural equation models are oneâ€¦ (More)

The goal of many sciences is to understand the mechanisms by w hich variables came to take on the values they have (that is, to find a generative model), and to predict what the values of thoseâ€¦ (More)

We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause, if such exists;â€¦ (More)

Previous asymptotically correct algorithms for recovering causal structure from sample probabilities have been limited even in sparse causal graphs to a few variables. We describe an asymptoticallyâ€¦ (More)

There is a long tradition of representing causal relationships by directed acyclic graphs (Wright, 1934). Spirtes (1994), Spirtes et al. (1993) and Pearl & Verma (1991) describe procedures forâ€¦ (More)