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Inferring the causal structure that links n observables is usually based upon detecting statistical dependences and choosing… Expand The causal Markov condition (CMC) is a postulate that links observations to causality. It describes the conditional independences… Expand Directed acyclic graph (DAG) models are popular tools for describing causal relationships and for guiding attempts to learn them… Expand It is still a matter of controversy whether the Principle of the Common Cause (PCC) can be used as a basis for sound causal… Expand Using cross-country data, the authors evaluate historical determinants of protection of property rights. They examine four… Expand The causal Markov condition (CMC) plays an important role in much recent work on the problem of causal inference from statistical… Expand This paper explores the relationship between a manipulability conception of causation and the causal Markov condition (CM). We… Expand In their rich and intricate paper ‘Independence, Invariance, and the Causal Markov Condition’, Daniel Hausman and James Woodward… Expand This essay explains what the Causal Markov Condition says and defends the condition from the many criticisms that have been… Expand This paper provides a priori cirteria for determing when a causal model is sufficiently complete to be considered a Bayesian… Expand