</HEAD> <H2> Choosing an Identifying Set of Matching or Conditioning Variables (with Kevin Quinn) </H2> <br> <br> Political scientists estimate average causal effects with regression or matching techniques, but both techniques require the user to choose a set of matching or conditioning variables. In this paper, we show that the standard advice from both frameworks on how to choose an identifying set of variables is often insufficient and at times misleading. Furthermore, we argue that non-parametric structural equation models (NPSEMs) solve this problem by creating a framework that provides simple rules for choosing an identifying set and that this framework is consistent with both the standard regression and Neyman-Rubin models. Furthermore, NPSEMs allow the specification of causal modeling assumptions in a manner that is compatible with the mechanistic view of causation commonly invoked by political scientists. <br> <br> <a href="./Backdoor.pdf">pdf</a> </BODY> </HTML>