The Miller Prize

The Miller Prize for is awarded for the best work appearing in Political Analysis the preceding year.

Past Recipients

2022 Winner
Recipients
Sherry Zaks (USC)
Work "Updating Bayesian(s): A Critical Evaluation of Bayesian Process Tracing"
Citation

We like this piece because carefully reveals a set of deep issues with Bayesian qualitative inference that were not appreciated with previous works in this area that readers of that literature would not normally appreciate. Dr. Zaks deftly takes on an established literature from a methodological perspective and reveals both strengths and weaknesses that are important to users are readers of the Bayesian process tracing literature. The abstract is pasted below.

Given the increasing quantity and impressive placement of work on Bayesian process tracing, this approach has quickly become a frontier of qualitative research methods. Moreover, it has dominated the process-tracing modules at the Institute for Qualitative and Multi-Method Research (IQMR) and the American Political Science Association (APSA) meetings for over five years, rendering its impact even greater. Proponents of qualitative Bayesianism make a series of strong claims about its contributions and scope of inferential validity. Four claims stand out: (1) it enables causal inference from iterative research, (2) the sequence in which we evaluate evidence is irrelevant to inference, (3) it enables scholars to fully engage rival explanations, and (4) it prevents ad hoc hypothesizing and confirmation bias. Notwithstanding the stakes of these claims and breadth of traction this method has received, no one has systematically evaluated the promises, trade-offs, and limitations that accompany Bayesian process tracing. This article evaluates the extent to which the method lives up to the mission. Despite offering a useful framework for conducting iterative research, the current state of the method introduces more bias than it corrects for on numerous dimensions. The article concludes with an examination of the opportunity costs of learning Bayesian process tracing and a set of recommendations about how to push the field forward.

Selection committee Yiqing Xu (Stanford), Libby Jenke (Duke), Cassy Dorf (Vanderbilt), Devin Caughey (MIT) and Jeff Gill (ex officio, American)
2021 Winner
Recipients
Reagan Mozer (Bentley University)
Luke Miratrix (Harvard)
Aaron Russell Kaufman (NYU Abu Dhabi)
L. Jason Anastasopoulos (University of Georgia)
Work "Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Quality"
Citation

On behalf of this year's Miller Prize committee (myself, Alexander Theodoridis, Patrick Brandt, and Jeff Gill), I’m delighted to announce the winner of the Society for Political Methodology’s 2021 Miller Prize for the best paper published in Political Analysis. This year the prize goes to the article "Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Quality," by Reagan Mozer, Luke Miratrix, Aaron Russell Kaufman, and L. Jason Anastasopoulos. The paper represents a significant advance in the important area of incorporating text data into a causal-inference framework. Please join us in congratulating the authors for this excellent piece of scholarship. The abstract is pasted below.

Matching for causal inference is a well-studied problem, but standard methods fail when the units to match are text documents: the high-dimensional and rich nature of the data renders exact matching infeasible, causes propensity scores to produce incomparable matches, and makes assessing match quality difficult. In this paper, we characterize a framework for matching text documents that decomposes existing methods into (1) the choice of text representation and (2) the choice of distance metric. We investigate how different choices within this framework affect both the quantity and quality of matches identified through a systematic multifactor evaluation experiment using human subjects. Altogether, we evaluate over 100 unique text-matching methods along with 5 comparison methods taken from the literature. Our experimental results identify methods that generate matches with higher subjective match quality than current state-of-the-art techniques. We enhance the precision of these results by developing a predictive model to estimate the match quality of pairs of text documents as a function of our various distance scores. This model, which we find successfully mimics human judgment, also allows for approximate and unsupervised evaluation of new procedures in our context. We then employ the identified best method to illustrate the utility of text matching in two applications. First, we engage with a substantive debate in the study of media bias by using text matching to control for topic selection when comparing news articles from thirteen news sources. We then show how conditioning on text data leads to more precise causal inferences in an observational study examining the effects of a medical intervention.

Selection committee Bear Braumoeller (Ohio State), Alexandar Theodoridis (UC, Merced), Patrick Brandt (UT, Dallas), and Jeff Gill (ex officio, American)
Year Recipient Work
2020 Jens Hainmueller (Stanford), Jonathan Mummolo (Princeton), Yiqing Xu (Stanford) “How Much Should We Trust Estimates from Multiplicative Interaction Models: Simple Tools to Improve Empirical Practice”
2019
Luke W. Miratrix (Harvard), Jasjeet S. Sekhon (UC Berkeley), Alexander G. Theodoridis (UC Merced), and Luis F. Campos (Harvard)
"Worth Weighting? How to Think About and Use Weights in Survey Experiments”
2018 Yiqing Xu "Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models"
2017 Joel A. Middleton (UC Berkeley), Mark A. Scott (NYU), Ronli Diakow (New York City Department of Education) and Jennifer L. Hill (NYU) "Bias Amplification and Bias Unmasking"
2016 Pablo Barberá "Birds ofthe same feather tweet together: Bayesian ideal point estimation using Twitter data"
2015 Jens Hainmueller (MIT), Dan Hopkins (Georgetown), and Teppi Yamamoto (MIT) "Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments"
2014 Jake Bowers (University of Illinois at Urbana-Champaign), Mark Fredrickson (University of Illinois at Urbana-Champaign), and Costas Panagopoulos (Fordham University) "Reasoning about Interference Between Units: A General Framework"
2013 Jens Hainmueller (MIT) "Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies"
2012 Devin Caughey and Jasjeet S. Sekhon (UC Berkeley) "Elections and the Regression-Discontinuity Design: Lessons from Close U.S. House Races"
2011 Justin Grimmer (Stanford) "A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases"
2010 Daniel Corstange (University of Maryland) "Sensitive Questions, Truthful Answers? Modeling the List Experiment with LISTIT"
2009 Muhammet Ali Bas (Harvard), Curtis S. Signorino (University of Rochester) and Robert W Walker (Washington University in St. Louis) "Statistical Backwards Induction: A Simple Method for Estimating Recursive Strategic Models"
2008 Daniel E. Ho (Stanford), Kosuke Imai (Princeton), Gary King (Harvard), Elizabeth A. Stuart (Johns Hopkins) "Matching as Nonparametric Preprocessing for Reduced Model Dependence in Parametric Causal Inference"
2007 Frederick J. Boehmke (University of Iowa) "The Influence of Unobserved Factors on Position Timing and Content in the NAFTA Vote"
2006 Robert J. Franzese, Jr. (University of Michigan) "Empirical Strategies for Various Manifestations of Multilevel Data"
2005 David W. Nickerson (Notre Dame) "Scalable Protocols Offer Efficient Design for Field Experiments"
2004 David K. Park, Andrew Gelman, and Joseph Bafumi (Columbia) "Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls"
2003 Jeffrey B. Lewis and Kenneth A. Schultz (UCLA) "Revealing Preferences: Empirical Estimation of a Crisis Bargaining Game with Incomplete Information"
2002 Patrick Heagerty (University of Washington), Michael D. Ward (University of Washington) and Kristian Skrede Gleditsch (UCSD) "Windows of Opportunity: Window Subseries Empirical Variance Estimators in International Relations"
2001 Keith T. Poole (University of Houston) "The Geometry of Multidimensional Quadratic Utility in Models of Parliamentary Roll Call Voting"
2000 John Londregan (UCLA) "Estimating Legislator’s Preferred Points"

Past Selection Committees

Year Committee
2020 Bear Braumoeller (Ohio State), Alexandar Theodoridis (UC, Merced), Patrick Brandt  (UT, Dallas), and Jeff Gill (ex officio, American)
2019 Pablo Babera (LSE), Jennifer Pan (Stanford), and Jeff Gill (American University)
2018 Jennifer Pan (Stanford), Pablo Barberá (LSE), and Jonathan Katz (CalTech)
2017 Patrick Brandt (UT Dallas, chair), Devin Caughey (MIT), Sunshine Hillygus (Duke) and Michael Alvarez (Cal Tech, ex officio)
2016 Neil Malhotra (Stanford, chair), Megan Shannon (Colorado), Arthur Spirling (NYU) and Thad Dunning (UC Berkeley)
2015 Neil Malhotra (Chair), Thad Dunning, Meg Shannon, Arthur Spirling
2014 David Nickerson (Chair), Devin Caughey, Justin Grimmer, Brad Jones
2013 David Nickerson (Chair), Devin Caughey, Justin Grimmer, Brad Jones
2012 Burt Monroe (Chair), Justin Grimmer, David Nickerson, Greg Wawro
2011 Dan Wood (Chair), Kosuke Imai, Greg Wawro, Burt Monroe
2010 Dan Wood (Chair), Kosuke Imai, Greg Wawro, Burt Monroe
2009 Dan Wood (Chair), Kosuke Imai, Greg Wawro, Burt Monroe
2008 Tobin Grant (Chair), David Darmofal (winner from previous year), Michael Hanmer, Orit Kedar, Drew Linzer
2007 Brian Pollins (Chair), Robert Franzese (winner from previous year), William Berry
2006 Brian Pollins (Chair), David Nickerson (winner from previous year), Stanley Feldman