Working Paper

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.

Keywords: Structural Equation; Potential Outcome; Invariance; Autonomy;

JEL Classification: C00; C01; C31;

https://doi.org/10.26509/frbc-wp-201505

Bibliographic Information

Provider: Federal Reserve Bank of Cleveland

Part of Series: Working Papers (Old Series)

Publication Date: 2015-03-27

Number: 1505