389 citations · 2.3k across the 33 of their papers we have counts for
8 papers · 2 filters
Causal Inference in the Presence of Latent Variables and Selection Bias
Peter L. Spirtes, Christopher Meek, Thomas S. Richardson
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…
Strong Completeness and Faithfulness in Bayesian Networks
Christopher Meek
A completeness result for d-separation applied to discrete Bayesian networks is presented and it is shown that in a strong measure-theoretic sense almost all discrete distributions…
Causal Inference and Causal Explanation with Background Knowledge
Christopher Meek
This paper presents correct algorithms for answering the following two questions; (i) Does there exist a causal explanation consistent with a set of background knowledge which expl…
Structure and Parameter Learning for Causal Independence and Causal Interaction Models
Christopher Meek, David Heckerman
This paper discusses causal independence models and a generalization of these models called causal interaction models. Causal interaction models are models that have independent me…
Quantifier Elimination for Statistical Problems
Dan Geiger, Christopher Meek
Recent improvement on Tarski's procedure for quantifier elimination in the first order theory of real numbers makes it feasible to solve small instances of the following problems c…
Dependency Networks for Collaborative Filtering and Data Visualization
David Heckerman, David Maxwell Chickering, Christopher Meek +2
We describe a graphical model for probabilistic relationships---an alternative to the Bayesian network---called a dependency network. The graph of a dependency network, unlike a Ba…