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

  • William Roberts Clark (a1) and Matt Golder (a2)

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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.
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.
Angrist, Joshua D.Jörn-Steffen, Pischke. 2009. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton, NJ: Princeton University Press.
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.
Berry, William, Golder, Matt, and Milton, Daniel. 2012. “Improving Tests of Theories Positing Interaction.” Journal of Politics 74: 653–71.
Brambor, Thomas, Roberts Clark, William, and Golder, Matt. 2006. “Understanding Interaction Models: Improving Empirical Analyses.” Political Analysis 14: 6382.
Deaton, Angus. 2010. “Instruments, Randomization, and Learning about Development.” Journal of Economic Literature 48 (June): 424–55.
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.
Huber, John. 2013. “Is Theory Getting Lost in the ‘Identification Revolution’?The Political Economist. Summer: 1–3.
Keele, Luke. 2015. “The Discipline of Identification.” PS: Political Science and Politics 48 (1): this issue.
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.
Manski, Charles F. 2013. Public Policy in an Uncertain World: Analysis and Decisions. Cambridge, MA: Harvard University Press.
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.
Nagler, Jonathan, and Tucker, Joshua A.. 2015. “Drawing Inferences and Testing Theories with Big Data.” PS: Political Science and Politics 48 (1): this issue.
Patty, John W., and Penn, Elizabeth Maggie. 2015. “Analyzing Big Data: Social Choice and Measurement. PS: Political Science and Politics 48 (1): this issue.
Poole, Keith T., and Rosenthal, Howard. 1997. Congress: A Political-Economic History of Roll Call Voting. New York: Oxford University Press.
Popper, Sir Karl. [1959] 2003. The Logic of Scientific Discovery. New York: Routledge.
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.
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.
Shadish, William R., Cook, Thomas D., and Campbell, Donald T.. 2002. Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Belmont, CA: Wadsworth.
Titiunik, Rocío. 2015. “Can Big Data Solve the Fundamental Problem of Causal Inference?PS: Political Science and Politics 48 (1): this issue.

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

  • William Roberts Clark (a1) and Matt Golder (a2)

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