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New text as data techniques offer a great promise: the ability to inductively discover measures that are useful for testing social science theories of interest from large collections of text.
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“Extracting policy positions from political texts using words as data.” American Political Science Review
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“Psychological aspects of natural language use: Our words, our selves.” Annual review of psychology
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Content analysis: An introduction to its methodology
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“The CMU 2008 Political Blog Corpus.”
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Variational inference for the Indian buffet process. In International Conference on Artificial Intelligence and Statistics
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The Changing Evidence Base of Social Science Research
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Causality
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“A method of automated nonparametric content analysis for social science.” American Journal of Political Science
Hopkins, Daniel J and Gary King. 2010 · 2010
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“How to analyze political attention with minimal assumptions and costs.” American Journal of Political Science
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“The indian buffet process: An introduction and review.” Journal of Machine Learning Research
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“False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant.” Psychological science
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Targeted learning: causal inference for observational and experimental data
van der Laan, Mark J and Sherri Rose. 2011 · 2011
Cited alongside, same era.
“Probabilistic topic models.” Communications of the ACM
Blei, David M. 2012 · 2012
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Field experiments: Design, analysis, and interpretation
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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
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A practical algorithm for topic modeling with provable guarantees. In International Conference on Machine Learning
Arora, Sanjeev, Rong Ge, Yonatan Halpern, David Mimno, Ankur Moitra, David Sontag, Yichen Wu and Michael Zhu. 2013 · 2013
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“Causal inference for ordinal outcomes.” arXiv preprint arXiv:1501.01234
Volfovsky, Alexander, Edoardo M Airoldi and Donald B Rubin. 2015 · 2015
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“Lasso adjustments of treatment effect estimates in randomized experiments.” Proceedings of the National Academy of Sciences
Bloniarz, Adam, Hanzhong Liu, Cun-Hui Zhang, Jasjeet S Sekhon and Bin Yu. 2016 · 2016
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“From Pork to Policy: The Rise of Programmatic Campaigning in Japanese Elections.” The Journal of Politics
Catalinac, Amy. 2016 · 2016
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Discovery of Treatments from Text Corpora. In Association of Computational Linguistics
Fong, Christian and Justin Grimmer. 2016 · 2016
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The content analysis guidebook
Neuendorf, Kimberly A. 2016 · 2016
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Making the news: Politics, the media, and agenda setting
Boydstun, Amber E. 2013 · 2013
Cited alongside, same era.
“Text as data: The promise and pitfalls of automatic content analysis methods for political texts.” Political analysis
Grimmer, Justin and Brandon M Stewart. 2013 · 2013
Cited alongside, same era.
“Causal inference in conjoint analysis: Understanding multidimensional choices via stated preference experiments.” Political Analysis
Hainmueller, Jens, Daniel J Hopkins and Teppei Yamamoto. 2013 · 2013
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“Fishing, commitment, and communication: A proposal for comprehensive nonbinding research registration.” Political Analysis
Humphreys, Macartan, Raul Sanchez de la Sierra and Peter Van der Windt. 2013 · 2013
Cited alongside, same era.
“Causal inference in latent class analysis.” Structural equation modeling: a multidisciplinary journal
Lanza, Stephanie T, Donna L Coffman and Shu Xu. 2013 · 2013
Cited alongside, same era.
“Statistical inference for data adaptive target parameters.”
van der Laan, Mark J, Alan E Hubbard and Sara Kherad Pajouh. 2013 · 2013
Cited alongside, same era.
Representing the advantaged: How politicians reinforce inequality
Butler, Daniel M. 2014 · 2014
Cited alongside, same era.
Navigating the Local Modes of Big Data: The Case of Topic Models. In Computational Social Science: Discovery and Prediction
Roberts, Margaret E., Brandon M. Stewart and Dustin Tingley. 2016 · 2016
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“A model of text for experimentation in the social sciences.” Journal of the American Statistical Association
Roberts, Margaret E, Brandon M. Stewart and Edoardo M Airoldi. 2016 · 2016
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Split-Sample Strategies for Avoiding False Discoveries. Technical report National Bureau of Economic Research
Anderson, Michael L and Jeremy Magruder. 2017 · 2017
Later among the works it cites.
“Double/debiased machine learning for treatment and structural parameters.” The Econometrics Journal
Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey and James Robins. 2017 · 2017
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“How Responsive are Political Elites? A Meta-Analysis of Experiments on Public Officials.” Journal of Experimental Political Science
Costa, Mia. 2017 · 2017
Later among the works it cites.
“Using Split Samples to Improve Inference on Causal Effects.” Political Analysis
Fafchamps, Marcel and Julien Labonne. 2017 · 2017
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texteffect: Discovering Latent Treatments in Text Corpora and Estimating Their Causal Effects
Fong, Christian. 2017 · 2017
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MTurkR: Access to Amazon Mechanical Turk Requester API via R
Leeper, Thomas J. 2017 · 2017
Later among the works it cites.
stm: R Package for Structural Topic Models
Roberts, Margaret E., Brandon M. Stewart and Dustin Tingley. 2017 · 2017
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Matching methods for high-dimensional data with applications to text. Technical report Working paper
Roberts, Margaret E, Brandon M. Stewart and Richard Nielsen. 2017 · 2017
Later among the works it cites.
Bit by bit: social research in the digital age
Salganik, Matthew J. 2017 · 2017
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Wager, Stefan and Susan Athey. 2017 · 2017
Later among the works it cites.
“Exploratory and Confirmatory Causal Inference for High Dimensional Interventions.”
Fong, Christian and Justin Grimmer. 2018 · 2018
Closest in time.
Causal inference
Hernan, Miguel A and James M Robins. 2018 · 2018
Closest in time.