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

TL;DR

This paper systematically evaluates methods for matching text documents in causal inference, identifying approaches that improve match quality and developing a predictive model to estimate match quality based on human judgments.

Contribution

It introduces a framework for text matching, conducts a comprehensive evaluation of over 100 methods, and develops a predictive model for match quality assessment.

Findings

Certain methods outperform existing techniques in subjective match quality

A predictive model successfully mimics human judgment of match quality

Text matching improves causal inference in media bias and medical studies

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. 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.