Title:      Non-parametric Mechanisms and Causal Modeling

Authors:    Adam Glynn, Kevin Quinn

Entrydate:  2007-07-15 14:57:07

Keywords:   Neyman-Rubin model, non-parametric structural equations,
causal inference, covariate selection, unmeasured confounding

Abstract:   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?  

http://polmeth.wustl.edu/retrieve.php?id=703