Causal Inference and Causal Explanation with Background Knowledge
This work addresses foundational issues in causal inference for researchers in statistics and machine learning, but appears incremental as it builds on existing causal explanation frameworks.
The paper tackles the problem of determining the existence and commonalities of causal explanations consistent with background knowledge and observed data, presenting correct algorithms to answer these questions.
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 explains all of the observed independence facts in a sample? (ii) Given that there is such a causal explanation what are the causal relationships common to every such causal explanation?