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A Distinction between Causal Effects in Structural and Rubin Causal Models


Structural Causal Models defi ne causal effects in terms of a single Data Generating Process (DGP), and the Rubin Causal Model defi nes causal effects in terms of a model that can represent counterfactuals from many DGPs. Under these different defi nitions, 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 codes: C00, C01, C31, C45.


Suggested citation: Aliprantis, Dionissi, 2015. “A Distinction between Causal Effects in Structural and Rubin Causal Models,” Federal Reserve Bank of Cleveland, Working Paper no. 15-05.

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