The Warren Miller Prize for the best article published in Volume 15
of *Political
Analysis* has been awarded to Daniel E. Ho, Kosuke Imai, Gary King, and
Elizabeth A. Stuart for their article, "Matching as Nonparametric
Preprocessing for Reducing Model Dependence in Parametric Causal Inference."
(Vol 15: 199-236).  The Miller Prize carries with it a $500 award funded by
Oxford University Press.  The prize will be awarded to the authors at the
Annual Meeting of the American Political Science Association.



The award committee consisted of Tobin Grant (Southern Illinois University,
chair), David Darmofal (University of South Carolina), Michael J. Hanmer
(University of Maryland), Orit Kedar (Massachusetts Institute of
Technology), and Drew Linzer (Emory University).



*Abstract*



Although published works rarely include causal estimates from more than a
few model specifications, authors usually choose the presented estimates
from numerous trial runs readers never see. Given the often 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 it is possible to find a
specification that fits the author's favorite hypothesis? And how do we
evaluate or even define statistical properties like unbiasedness or mean
squared error when no unique model or estimator even exists? Matching
methods, which offer the promise of causal inference with fewer assumptions,
constitute one possible way forward, but crucial results in this
fast-growing methodological literature are often grossly misinterpreted. We
explain how to avoid these misinterpretations and propose a unified approach
that makes it possible for researchers to preprocess data with matching
(such as with the easy-to-use software we offer) and then to apply the best
parametric techniques they would have used anyway. This procedure makes
parametric models produce more accurate and considerably less
model-dependent causal inferences.



*Author Information*



Daniel E. Ho

Stanford Law School, 559 Nathan Abbott Way, Stanford, CA 94305
e-mail: [log in to unmask]



Kosuke Imai

Department of Politics, Princeton University, Princeton, NJ 08544
e-mail: [log in to unmask]



Gary King

Department of Government, Harvard University, 1737 Cambridge Street,
Cambridge, MA 02138

e-mail: [log in to unmask]



Elizabeth A. Stuart

Departments of Mental Health and Biostatistics, Johns Hopkins Bloomberg
School of Public Health, 624 North Broadway, Room 804, Baltimore, MD 21205
e-mail: [log in to unmask]

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