A Distinction between Causal Effects in Structural and Rubin Causal Models

11 Pages Posted: 1 Apr 2015

See all articles by Dionissi Aliprantis

Dionissi Aliprantis

Federal Reserve Banks - Federal Reserve Bank of Cleveland

Date Written: March 27, 2015

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

JEL Classification: C00, C01, C31, C45.

Suggested Citation

Aliprantis, Dionissi, A Distinction between Causal Effects in Structural and Rubin Causal Models (March 27, 2015). FRB of Cleveland Working Paper No. 15-05, Available at SSRN: https://ssrn.com/abstract=2587076 or http://dx.doi.org/10.2139/ssrn.2587076

Dionissi Aliprantis (Contact Author)

Federal Reserve Banks - Federal Reserve Bank of Cleveland ( email )

East 6th & Superior
Cleveland, OH 44101-1387
United States

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