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Big Data, Causal Inference, and Formal Theory: Contradictory Trends in Political Science?

Introduction

Published online by Cambridge University Press:  31 December 2014

William Roberts Clark
Affiliation:
Texas A&M University
Matt Golder
Affiliation:
Pennsylvania State University

Abstract

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Type
Symposium: Big Data, Causal Inference, and Formal Theory: Contradictory Trends in Political Science?
Copyright
Copyright © American Political Science Association 2015 

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References

REFERENCES

Anderson, Chris. 2008. “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete.” Wired Magazine. http://archive.wired.com/science/discoveries/magazine/16-07/pb_theory.Google Scholar
Angrist, Joshua D., and Pischke, Jörn-Steffen. 2010. “The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics.” Journal of Economic Perspectives 24 (2): 330.Google Scholar
Angrist, Joshua D.Jörn-Steffen, Pischke. 2009. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton, NJ: Princeton University Press.Google Scholar
Ashworth, Scott, Berry, Christopher, and Mesquita, Ethan Bueno de. 2015. “All Else Equal in Theory and Data (Big or Small).” PS: Political Science and Politics 48 (1): this issue.Google Scholar
Berry, William, Golder, Matt, and Milton, Daniel. 2012. “Improving Tests of Theories Positing Interaction.” Journal of Politics 74: 653–71.Google Scholar
Brambor, Thomas, Roberts Clark, William, and Golder, Matt. 2006. “Understanding Interaction Models: Improving Empirical Analyses.” Political Analysis 14: 6382.CrossRefGoogle Scholar
Deaton, Angus. 2010. “Instruments, Randomization, and Learning about Development.” Journal of Economic Literature 48 (June): 424–55.Google Scholar
Grimmer, Justin. 2015. “We Are All Social Scientists Now: How Big Data, Machine Learning, and Causal Inference Work Together.” PS: Political Science and Politics 48 (1): this issue.Google Scholar
Huber, John. 2013. “Is Theory Getting Lost in the ‘Identification Revolution’?The Political Economist. Summer: 1–3.Google Scholar
Keele, Luke. 2015. “The Discipline of Identification.” PS: Political Science and Politics 48 (1): this issue.Google Scholar
King, Gary. 2014. “Restructuring the Social Sciences: Reflections from Harvard’s Institute for Quantitative Social Science.” PS: Political Science and Politics 47 (1): 165–72.Google Scholar
Manski, Charles F. 2013. Public Policy in an Uncertain World: Analysis and Decisions. Cambridge, MA: Harvard University Press.CrossRefGoogle Scholar
Monroe, Burt L., Pan, Jennifer, Roberts, Margaret E., Sen, Maya, and Sinclair, Betsy. 2015. “No! Formal Theory, Causal Inference, and Big Data Are Not Contradictory Trends in Political Science.” PS: Political Science and Politics 48 (1): this issue.Google Scholar
Nagler, Jonathan, and Tucker, Joshua A.. 2015. “Drawing Inferences and Testing Theories with Big Data.” PS: Political Science and Politics 48 (1): this issue.Google Scholar
Patty, John W., and Penn, Elizabeth Maggie. 2015. “Analyzing Big Data: Social Choice and Measurement. PS: Political Science and Politics 48 (1): this issue.Google Scholar
Poole, Keith T., and Rosenthal, Howard. 1997. Congress: A Political-Economic History of Roll Call Voting. New York: Oxford University Press.Google Scholar
Popper, Sir Karl. [1959] 2003. The Logic of Scientific Discovery. New York: Routledge.Google Scholar
Sekhon, Jasjeet. 2010. “The Neyman-Rubin Model of Causal Inference and Estimation Via Matching Methods.” In The Oxford Handbook of Political Methdology. Eds. Box-Steffensmeier, Janet M., Brady, Henry E., and Collier, David. New York: Oxford University Press.Google Scholar
Shadish, William R. 2010. “Campbell and Rubin: A Primer and Comparison of Their Approaches to Causal Inference in Field Settings.” Psychological Methods 15 (1): 317.Google Scholar
Shadish, William R., Cook, Thomas D., and Campbell, Donald T.. 2002. Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Belmont, CA: Wadsworth.Google Scholar
Titiunik, Rocío. 2015. “Can Big Data Solve the Fundamental Problem of Causal Inference?PS: Political Science and Politics 48 (1): this issue.Google Scholar