A distinction between causal effects in structural and rubin causal models
No 1505, Working Papers (Old Series) from Federal Reserve Bank of Cleveland
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.
Keywords: Structural Equation; Potential Outcome; Invariance; Autonomy (search for similar items in EconPapers)
JEL-codes: C00 C01 C31 (search for similar items in EconPapers)
Pages: 11 pages
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