Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets

Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020)

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Authors

Daniel Kumor, Carlos Cinelli, Elias Bareinboim

Abstract

<p>We develop a a new polynomial-time algorithm for identification in linear Structural Causal Models that subsumes previous non-exponential identification methods when applied to direct effects, and unifies several disparate approaches to identification in linear systems. Leveraging these new results and understanding, we develop a procedure for identifying total causal effects. </p>