A distinction between causal effects in structural and rubin causal models
Abstract: Structural Causal Models define causal effects in terms of a single Data Generating Process (DGP), and the Rubin Causal Model defines causal effects in terms of a model that can represent counterfactuals from many DGPs. Under these different definitions, notationally similar causal effects make distinct claims about the results of interventions to the system under investigation: Structural equations imply conditional independencies in the data that potential outcomes do not. One implication is that the DAG of a Rubin Causal Model is different from the DAG of a Structural Causal Model. Another is that Pearl?s do-calculus does not apply to potential outcomes and the Rubin Causal Model.
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Provider: Federal Reserve Bank of Cleveland
Part of Series: Working Papers (Old Series)
Publication Date: 2015-03-27