Justin Ryan Grimmer
Morris M. Doyle Centennial Professor of Public Policy and Senior Fellow at the Hoover Institution
Political Science
Web page: http://www.justingrimmer.org
Bio
Justin Grimmer is the Morris M. Doyle Centennial Professor in Public Policy in Stanford University's Department of Political Science, Senior Fellow at the Hoover Institution, and Co-Director of the Democracy and Polarization Lab. His research focuses on Congress, elections, social media, and data science.
2024-25 Courses
- Political Methodology III: Model-Based Inference
POLISCI 450C (Spr) - Workshop in Political Methodology
POLISCI 353B (Win) - Workshop in Political Methodology
POLISCI 353C (Spr) -
Independent Studies (4)
- Advanced Individual Study in Political Methodology
POLISCI 359 (Spr) - Directed Reading and Research in American Politics
POLISCI 229 (Spr) - Directed Reading and Research in American Politics
POLISCI 329 (Spr) - Directed Reading and Research in Political Methodology
POLISCI 259 (Spr)
- Advanced Individual Study in Political Methodology
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Prior Year Courses
2023-24 Courses
- Controversies in American Public Policy
POLISCI 127 (Win) - Political Methodology III: Model-Based Inference
POLISCI 450C (Spr) - Workshop in American Politics
POLISCI 422 (Aut, Win, Spr) - Workshop in Political Methodology
POLISCI 353B (Win)
2022-23 Courses
- Political Methodology III: Model-Based Inference
POLISCI 450C (Spr) - The Science of Politics
POLISCI 1 (Aut, Sum) - Workshop in Political Methodology
POLISCI 353C (Spr)
2021-22 Courses
- American Political Institutions
POLISCI 420A (Win) - How to Write and Publish a Quantitative Political Science Paper
POLISCI 462 (Spr) - Political Methodology III: Model-Based Inference
POLISCI 450C (Spr) - The Science of Politics
POLISCI 1 (Aut) - What's Wrong with American Government? An Institutional Approach
POLISCI 120Z (Sum)
- Controversies in American Public Policy
Stanford Advisees
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Doctoral Dissertation Reader (AC)
Albert Chiu, Chris Flores, Alena Smith -
Doctoral Dissertation Co-Advisor (AC)
Cole Tanigawa-Lau, Jennifer Wu, Chenoa Yorgason -
Doctoral (Program)
Liam Bethlendy, Jiehan Liu, Andrew Myers, Abhinav Ramaswamy, Kasey Rhee, Jennifer Wu
All Publications
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Who Are the Election Skeptics? Evidence from the 2022 Midterm Elections
ELECTION LAW JOURNAL
2024
View details for DOI 10.1089/elj.2024.0010
View details for Web of Science ID 001316152600001
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Evaluating a New Generation of Expansive Claims about Vote Manipulation
ELECTION LAW JOURNAL
2024
View details for DOI 10.1089/elj.2022.0070
View details for Web of Science ID 001223971500001
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How Election Rules Affect Who Wins
JOURNAL OF LEGAL ANALYSIS
2024; 16 (1): 1-25
View details for DOI 10.1093/jla/laae001
View details for Web of Science ID 001197285800001
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Legislator Advocacy on Behalf of Constituents and Corporate Donors: A Case Study of the Federal Energy Regulatory Commission
ACCOUNTABILITY RECONSIDERED
2023: 265-294
View details for Web of Science ID 001064359800012
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Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding
JOURNAL OF MACHINE LEARNING RESEARCH
2023; 24
View details for Web of Science ID 001111683500001
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How to make causal inferences using texts.
Science advances
2022; 8 (42): eabg2652
Abstract
Text as data techniques offer a great promise: the ability to inductively discover measures that are useful for testing social science theories with large collections of text. Nearly all text-based causal inferences depend on a latent representation of the text, but we show that estimating this latent representation from the data creates underacknowledged risks: we may introduce an identification problem or overfit. To address these risks, we introduce a split-sample workflow for making rigorous causal inferences with discovered measures as treatments or outcomes. We then apply it to estimate causal effects from an experiment on immigration attitudes and a study on bureaucratic responsiveness.
View details for DOI 10.1126/sciadv.abg2652
View details for PubMedID 36260669
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Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond
TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS
2022; 10: 1138-1158
View details for DOI 10.1162/tacl_a_00511
View details for Web of Science ID 000923425200002
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Reply to Kalmoe and Mason: The pitfalls of using surveys to measure low-prevalence attitudes and behavior.
Proceedings of the National Academy of Sciences of the United States of America
2022; 119 (32): e2207584119
View details for DOI 10.1073/pnas.2207584119
View details for PubMedID 35878008
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Partisan Enclaves and Information Bazaars: Mapping Selective Exposure to Online News
JOURNAL OF POLITICS
2022
View details for DOI 10.1086/716950
View details for Web of Science ID 000752056100003
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Current research overstates American support for political violence.
Proceedings of the National Academy of Sciences of the United States of America
2022; 119 (12): e2116870119
Abstract
SignificanceRecent political events show that members of extreme political groups support partisan violence, and survey evidence supposedly shows widespread public support. We show, however, that, after accounting for survey-based measurement error, support for partisan violence is far more limited. Prior estimates overstate support for political violence because of random responding by disengaged respondents and because of a reliance on hypothetical questions about violence in general instead of questions on specific acts of political violence. These same issues also cause the magnitude of the relationship between previously identified correlates and partisan violence to be overstated. As policy makers consider interventions designed to dampen support for violence, our results provide critical information about the magnitude of the problem.
View details for DOI 10.1073/pnas.2116870119
View details for PubMedID 35302889
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How Does the Rising Number of Women in the US Congress Change Deliberation? Evidence from House Committee Hearings
QUARTERLY JOURNAL OF POLITICAL SCIENCE
2022; 17 (3): 355-387
View details for DOI 10.1561/100.00020112
View details for Web of Science ID 000910177200002
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No evidence for systematic voter fraud: A guide to statistical claims about the 2020 election.
Proceedings of the National Academy of Sciences of the United States of America
2021; 118 (45)
Abstract
After the 2020 US presidential election Donald Trump refused to concede, alleging widespread and unparalleled voter fraud. Trump's supporters deployed several statistical arguments in an attempt to cast doubt on the result. Reviewing the most prominent of these statistical claims, we conclude that none of them is even remotely convincing. The common logic behind these claims is that, if the election were fairly conducted, some feature of the observed 2020 election result would be unlikely or impossible. In each case, we find that the purportedly anomalous fact is either not a fact or not anomalous.
View details for DOI 10.1073/pnas.2103619118
View details for PubMedID 34728563
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Causal Inference with Latent Treatments
AMERICAN JOURNAL OF POLITICAL SCIENCE
2021
View details for DOI 10.1111/ajps.12649
View details for Web of Science ID 000695052200001
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The durable differential deterrent effects of strict photo identification laws
POLITICAL SCIENCE RESEARCH AND METHODS
2022; 10 (3): 453-469
View details for DOI 10.1017/psrm.2020.57
View details for Web of Science ID 000792151300001
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Machine Learning for Social Science: An Agnostic Approach
ANNUAL REVIEW OF POLITICAL SCIENCE, VOL 24, 2021
2021; 24: 395-419
View details for DOI 10.1146/annurev-polisci-053119-015921
View details for Web of Science ID 000652490700019
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Political cultures: measuring values heterogeneity
POLITICAL SCIENCE RESEARCH AND METHODS
2020; 8 (3): 571–79
View details for DOI 10.1017/psrm.2019.43
View details for Web of Science ID 000540277500013
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Cyberwar: How Russian Hackers and Trolls Helped Elect a President-What We Don't, Can't, and Do Know (Book Review)
PUBLIC OPINION QUARTERLY
2019; 83 (1): 159–63
View details for DOI 10.1093/poq/nfy049
View details for Web of Science ID 000469811800009
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Mirrors for Princes and Sultans: Advice on the Art of Governance in the Medieval Christian and Islamic Worlds
JOURNAL OF POLITICS
2018; 80 (4): 1150–67
View details for DOI 10.1086/699246
View details for Web of Science ID 000445660100009
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Obstacles to Estimating Voter ID Laws' Effect on Turnout
JOURNAL OF POLITICS
2018; 80 (3): 1045–51
View details for DOI 10.1086/696618
View details for Web of Science ID 000436308500032
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Money in Exile: Campaign Contributions and Committee Access
JOURNAL OF POLITICS
2016; 78 (4): 974-988
View details for DOI 10.1086/686615
View details for Web of Science ID 000384884200024
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Discovery of Treatments from Text Corpora
ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2016: 1600-1609
View details for Web of Science ID 000493806800151
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We Are All Social Scientists Now: How Big Data, Machine Learning, and Causal Inference Work Together
PS-POLITICAL SCIENCE & POLITICS
2015; 48 (1): 80-83
View details for DOI 10.1017/S1049096514001784
View details for Web of Science ID 000347159500028
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Elevated threat levels and decreased expectations: How democracy handles terrorist threats
POETICS
2013; 41 (6): 650-669
View details for DOI 10.1016/j.poetic.2013.06.003
View details for Web of Science ID 000329558200005
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Congressmen in Exile: The Politics and Consequences of Involuntary Committee Removal
JOURNAL OF POLITICS
2013; 75 (4): 907-920
View details for DOI 10.1017/S0022381613000704
View details for Web of Science ID 000325771600017
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Multinomial Inverse Regression for Text Analysis Comment
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
2013; 108 (503): 770-771
View details for DOI 10.1080/01621459.2013.822383
View details for Web of Science ID 000325782300002
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Appropriators not Position Takers: The Distorting Effects of Electoral Incentives on Congressional Representation
AMERICAN JOURNAL OF POLITICAL SCIENCE
2013; 57 (3): 624-642
View details for DOI 10.1111/ajps.12000
View details for Web of Science ID 000321109300008
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Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
POLITICAL ANALYSIS
2013; 21 (3): 267-297
View details for DOI 10.1093/pan/mps028
View details for Web of Science ID 000321825000001
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How Words and Money Cultivate a Personal Vote: The Effect of Legislator Credit Claiming on Constituent Credit Allocation
AMERICAN POLITICAL SCIENCE REVIEW
2012; 106 (4): 703-719
View details for DOI 10.1017/S0003055412000457
View details for Web of Science ID 000311519300002
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General purpose computer-assisted clustering and conceptualization
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2011; 108 (7): 2643-2650
Abstract
We develop a computer-assisted method for the discovery of insightful conceptualizations, in the form of clusterings (i.e., partitions) of input objects. Each of the numerous fully automated methods of cluster analysis proposed in statistics, computer science, and biology optimize a different objective function. Almost all are well defined, but how to determine before the fact which one, if any, will partition a given set of objects in an "insightful" or "useful" way for a given user is unknown and difficult, if not logically impossible. We develop a metric space of partitions from all existing cluster analysis methods applied to a given dataset (along with millions of other solutions we add based on combinations of existing clusterings) and enable a user to explore and interact with it and quickly reveal or prompt useful or insightful conceptualizations. In addition, although it is uncommon to do so in unsupervised learning problems, we offer and implement evaluation designs that make our computer-assisted approach vulnerable to being proven suboptimal in specific data types. We demonstrate that our approach facilitates more efficient and insightful discovery of useful information than expert human coders or many existing fully automated methods.
View details for DOI 10.1073/pnas.1018067108
View details for Web of Science ID 000287377000009
View details for PubMedID 21292983
View details for PubMedCentralID PMC3041127
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An Introduction to Bayesian Inference via Variational Approximations
POLITICAL ANALYSIS
2011; 19 (1): 32-47
View details for DOI 10.1093/pan/mpq027
View details for Web of Science ID 000286572100003
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Approval regulation and endogenous consumer confidence: Theory and analogies to licensing, safety, and financial regulation
REGULATION & GOVERNANCE
2010; 4 (4): 383-407
View details for DOI 10.1111/j.1748-5991.2010.01091.x
View details for Web of Science ID 000284486300001