A Logical Characterization of Constraint-Based Causal Discovery

Abstract

We present a novel approach to constraint-based causal discovery, that takes the form of straightforward logical inference, applied to a list of simple, logical statements about causal relations that are derived directly from observed (in)dependencies. It is both sound and complete, in the sense that all invariant features of the corresponding partial ancestral graph (PAG) are identified, even in the presence of latent variables and selection bias. The approach shows that every identifiable causal relation corresponds to one of just two fundamental forms. More importantly, as the basic building blocks of the method do not rely on the detailed (graphical) structure of the corresponding PAG, it opens up a range of new opportunities, including more robust inference, detailed accountability, and application to large models.

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• Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables
• Definite Non-Ancestral Relations and Structure Learning
• Proof Supplement - Learning Sparse Causal Models is not NP-hard (UAI2013)