Title:      Why Process Matters for Causal Inference

Authors:    Adam Glynn, Kevin Quinn

Entrydate:  2009-07-13 17:23:12

Keywords:   causal effect, process, post-treatment, mechanism,
mediation, potential outcomes 

Abstract:    In this paper we provide a formal account of how
information about causal processes (i.e., knowledge of the
causal chain linking an explanatory variable to an outcome
variable) can be used to sharpen causal inferences. All of this
is done within a Bayesian potential outcomes causal model. The
methods discussed in this paper empower researchers by providing
them with a richer palette of causal assumptions than typically
employed. At the same time, because the methods are embedded in
a rigorous counterfactual causal model, researchers are held to
high standards of transparency and logical consistency. We
illustrate these methods with an application to the effects of
election day registration on African American turnout. This
analysis shows that traditional regression or matching estimates
for these effects are likely overstated. 

http://polmeth.wustl.edu/retrieve.php?id=915

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