</HEAD> <H2> Non-parameteric Mechanisms and Causal Modeling (with Kevin Quinn) </H2> <br> <br> 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? <br> <br> <a href="./GlynnQuinn07.pdf">pdf</a> </BODY> </HTML>