Causal Inference and Causal Explanation with Background Knowledge

TL;DR

This paper develops algorithms to determine the existence of causal explanations consistent with background knowledge and identifies causal relationships common to all such explanations.

Contribution

It introduces correct algorithms for causal explanation and causal relationship identification based on background knowledge and observed independence facts.

Findings

Algorithms successfully identify consistent causal explanations.

Methods determine causal relationships common to all explanations.

Framework integrates background knowledge with observed data.

Abstract

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?