Although political science articles rarely include causal
estimates from more than a few model specifications, authors
usually
choose
these from numerous trial runs readers never see. Given the
typically large variation in estimates across choices of control
variables, functional forms, and other modeling assumptions, how
can
researchers ensure that the few estimates presented are accurate
or
representative? How do readers know that publications are not
merely demonstrations that the author found it
possible
to
find a specification that fits his or her favorite hypothesis?
Matching methods, which offer the promise of causal inference
with
fewer assumptions, is one possible way forward, but the
literature
suffers from conflicting approaches to estimation, uncertainty,
theoretical results, and practical advice. We propose a unified
approach that makes it possible for researchers to preprocess
data
(such as with the
easy-to-use
software we offer) and then to
apply
whatever familiar parametric techniques they would have used
anyway.
Instead of replacing existing methods, we use matching to make
parametric models work better by giving more accurate and
considerably less model-dependent causal inferences.
(Last Revised October 13, 2004)