Methods

Agnostic Sensitivity Analysis

Christopher Schwarz (New York University)

Abstract: The threat of endogeneity is ubiquitous within applied empirical research. A `near Bayesian' method of sensitivity analysis is developed and implemented, overcoming a number of difficulties with existing approaches. The procedure targets the distribution of possible causal...

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Using Poisson Binomial Models to Reveal Voter Preferences

Evan Rosenman and Nitin Viswanathan (Stanford University)

Abstract: We consider a problem of ecological inference, in which individual-level covariates are known, but labeled data is available only at the aggregate level...

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Formalization of Political Analysis: Matrix of Possibles States and Strategies

Fernando Rocha Rosario (Universidad Nacional Autónoma de México)

Abstract: In this paper I expose a technique which formalizes the political analysis using modal logic and theory of rational choice. For represent the...

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A New Multilevel-Based Indicator for Party System Nationalization

Kazuma Mizukoshi (University College London)

Abstract: “Science is impossible without an evolving network of stable measures” (Wright 1997: 33), but to what extent should measures be stable? Though measures seem still stable as...

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The Heuristic Issue Voter: Issue Preferences and Candidate Choice

Gabriel Madson (Duke University)

Abstract: Issue voting, where citizens select candidates primarily for their positions on political issues, is a normatively appealing theory of voting. A public whose political behavior is driven by...

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Beyond Topics: Semi-Supervised Learning for Texts From a Measurement Perspective

Shiyao Liu (Massachusetts Institute of Technology)

Abstract: This project proposes a new methodological framework to use text data as a measurement in political science. Despite the abundance of text data available nowadays, conversion of text data into a measurement for a political concept remains a challenge that prevents...

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Latent Factor Approach to Missing Not at Random

Naijia Liu (Princeton University)

Abstract: Social scientists rely heavily on survey datasets to study important questions, such as policy preferences and voting intentions. However, it is common that respondents choose not to answer a certain question due to some unobserved confounders, thus causing ’missing not at random (MNAR)’...

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Causal Inference in Difference-in-Differences Designs under Uncertainty in Counterfactual Trends

Thomas Leavitt (Columbia University)

Abstract: Difference-in-Differences (DID) is a popular method for design-based causal inference. Design-based methods typically quantify uncertainty in inferences from a sample to a population via a sampling mechanism and from observed to counterfactual outcomes via an assignment mechanism. The...

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Clustering Large-Scale Ballot Data With Varying Choice Sets

Shiro Kuriwaki (Harvard University)

Abstract: Election scholars increasingly analyze large cast vote records (ballot image logs) to measure ticket splitting and ideological coherence in actual voter behavior. Election administrators also store cast vote records to detect election fraud and audit results. Although clustering methods...

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Paragraph-Citation Topic Models for Corpora With Citation Networks

ByungKoo Kim, Yuki Shiraito and Saki Kuzushima (University of Michigan)

Abstract: Social scientists often analyze a corpus with a citation network among its documents, such as the corpus of the U.S. Supreme Court decisions. Existing topic models for document networks assume that the topic of a citation is...

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Modeling Time and Space Together

Ali Kagalwala (Texas A&M University), Andrea Junqueira (Texas A&M University), Guy D. Whitten (Texas A&M University), Laron K. Williams (University of Missouri) and Cameron Wimpy (Arkansas State University)

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New Frontiers in Dynamic Pie Modeling

Andrea Junqueira (Texas A&M University), Ali Kagalwala (Texas A&M University), Andrew Philips (University of Colorado Boulder) and Guy Whitten (Texas A&M University)

Abstract: In this paper, we...

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A General Method for Detecting & Characterizing Interference in Field Experiments

Connor Jerzak (Harvard University)

Abstract: With the rise of online social networks, there has been growing interest in modeling how experimental units influence each another---a phenomenon known as "interference'' in the causal inference literature. Current models for interference generally requires knowledge of the way in which units are connected. Yet, in most field experiments, such data is unavailable. In this paper, we propose a...

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Leveraging Observational Outcomes to Improve the Generalization of Experimental Results

Melody Huang (University of California, Los Angeles), Erin Hartman (University of California, Los Angeles), Naoki Egami (Columbia University) and Luke Miratrix (Harvard University)

*Award for Best Graduate Student Poster - Methods*

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Extracting Political Events From Text Using Grammatical Dependency Parsing and Machine Learning

Andrew Halterman (Massachusetts Institute of Technology)

Abstract: This paper introduces a method that automatically extracts political events from text using grammatical parsing and machine learning. Much of the scientifically useful information about what political actors are doing is locked away in text. To extract this...

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