Non-parametric Mechanisms and Causal Modeling

Political scientists tend to think about causality in terms of mechanisms. In this paper we argue that non-parametric structural equation models are consistent with how many empirical political scientists think about causality and are consistent with the powerful and well-respected Neyman-Rubin Causal Model. Furthermore, using examples we demonstrate that two important practical questions are more easily addressed within the mechanistic framework: What (if any) set or sets of conditioning variables will allow the identification of average causal effects in a regression or matching model? When unmeasured confounding is present, what (if any) adjustment will non-parametrically identify the average causal effect? ∗The authors thank Thomas Richardson for introducing them to literature of graphical causal models. In addition, Quinn thanks the National Science Foundation (grants SES 03-50613 and BCS 05-27513) and the Center for Advanced Study in the Behavioral Sciences for its hospitality and support. The usual caveat applies. †Department of Government and The Institute for Quantitative Social Sciences Harvard University, 1737 Cambridge Street, Cambridge, MA 02138. [email protected] ‡Department of Government and The Institute for Quantitative Social Sciences Harvard University, 1737 Cambridge Street, Cambridge, MA 02138. kevin [email protected]