Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets
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.
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