A distinction between causal effects in structural and rubin causal models

Dionissi Aliprantis

No 1505, Working Papers (Old Series) from Federal Reserve Bank of Cleveland

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.

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
Date: 2015-03-27
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (3)

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DOI: 10.26509/frbc-wp-201505

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