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Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Quality

Published online by Cambridge University Press:  17 March 2020

Reagan Mozer Open the ORCID record for Reagan Mozer [Opens in a new window] ,
Luke Miratrix Open the ORCID record for Luke Miratrix [Opens in a new window] ,
Aaron Russell Kaufman Open the ORCID record for Aaron Russell Kaufman [Opens in a new window]  and
L. Jason Anastasopoulos Open the ORCID record for L. Jason Anastasopoulos [Opens in a new window]
Reagan Mozer*
Affiliation:
Bentley University, Department of Mathematical Sciences, Waltham, MA02452-4713, USA. Email: rmozer@bentley.edu
Luke Miratrix
Affiliation:
Harvard Graduate School of Education, Cambridge, MA02138, USA. Email: luke_miratrix@gse.harvard.edu
Aaron Russell Kaufman
Affiliation:
Division of Social Science, New York University Abu Dhabi, Saadiyat Island, Abu Dhabi, United Arab Emirates. Email: aaronkaufman@nyu.edu
L. Jason Anastasopoulos
Affiliation:
University of Georgia, Department of Public Administration and Policy and Political Science, Athens, GA30601, USA. Email: ljanastas@uga.edu
*
*Email: rmozer@bentley.edu
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Abstract

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.

Keywords

statistical analysis of textsmatching methodsobservational studies
Type
Articles
Information
Political Analysis , Volume 28 , Issue 4 , October 2020 , pp. 445 - 468
Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press on behalf of the Society for Political Methodology.

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Footnotes

Contributing Editor: Jeff Gill

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