Confounding Equivalence in Causal Inference

Details Confounding Equivalence in Causal Inference Confounding Equivalence in Causal Inference

2012-03-15

arxiv.org/abs/1203.3505v1

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Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010)

Scientific paper

The paper provides a simple test for deciding, from a given causal diagram, whether two sets of variables have the same bias-reducing potential under adjustment. The test requires that one of the following two conditions holds: either (1) both sets are admissible (i.e., satisfy the back-door criterion) or (2) the Markov boundaries surrounding the manipulated variable(s) are identical in both sets. Applications to covariate selection and model testing are discussed.

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