Big data, causal inference, and formal theory: Contradictory trends in political science?

PS - Political Science and Politics

Volumn 48, Issue 1, 2014, Pages 65-70

  Clark, William Roberts ^a   Golder, Matt ^b  

^a TEXAS A AND M UNIVERSITY   (United States)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84920159516     PISSN: 10490965     EISSN: 15375935     Source Type: Journal    
DOI: 10.1017/S1049096514001759     Document Type: Conference Paper

References (21)

1. Anderson, Chris. 2008. " The End of Theory: The Data Deluge Makes the Scientifi c Method Obsolete." Wired Magazine. http://archive.wired.com/science/discoveries/magazine/16-07/pb-theory.
2. Angrist, Joshua D., and Jörn-Steffen Pischke. 2010. " The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics." Journal of Economic Perspectives 24 (2): 3-30.
3. -. 2009. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ : Princeton University Press.
4. Ashworth, Scott, Christopher Berry, and Ethan Bueno de Mesquita. 2015. " All Else Equal in Theory and Data (Big or Small)." PS: Political Science and Politics 48 (1): this issue.
5. Berry, William, Matt Golder, and Daniel Milton. 2012. " Improving Tests of Theories Positing Interaction." Journal of Politics 74 : 653-71.
6. Brambor, Thomas, William Roberts Clark, and Matt Golder. 2006. " Understanding Interaction Models: Improving Empirical Analyses." Political Analysis 14 : 63-82.
7. Deaton, Angus. 2010. " Instruments, Randomization, and Learning about Development." Journal of Economic Literature 48 (June): 424-55.
8. 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.
9. Huber, John. 2013. " Is Theory Getting Lost in the 'Identifi cation Revolution'? " The Political Economist. Summer: 1-3.
10. Keele, Luke. 2015. " The Discipline of Identifi cation." PS: Political Science and Politics 48 (1): this issue.
11. King, Gary. 2014. " Restructuring the Social Sciences: Refl ections from Harvard's Institute for Quantitative Social Science." PS: Political Science and Politics 47 (1): 165-72.
12. Manski, Charles F. 2013. Public Policy in an Uncertain World: Analysis and Decisions. Cambridge, MA : Harvard University Press.
13. Monroe, Burt L., Jennifer Pan, Margaret E. Roberts, Maya Sen, and Betsy Sinclair. 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.
14. Nagler, Jonathan, and Joshua A. Tucker. 2015. " Drawing Inferences and Testing Theories with Big Data." PS: Political Science and Politics 48 (1): this issue.
15. Patty, John W., and Elizabeth Maggie Penn. 2015. " Analyzing Big Data: Social Choice and Measurement. PS: Political Science and Politics 48 (1): this issue.
16. Poole, Keith T., and Howard Rosenthal. 1997. Congress: A Political-Economic History of Roll Call Voting. New York : Oxford University Press.
17. Popper, Sir Karl. [1959] 2003. The Logic of Scientifi c Discovery. New York : Routledge.
18. Sekhon, Jasjeet. 2010. " The Neyman-Rubin Model of Causal Inference and Estimation Via Matching Methods." In The Oxford Handbook of Political Methdology. Eds. Janet M. Box-Steff ensmeier, Henry E. Brady, and David Collier. New York : Oxford University Press.
19. Shadish, William R. 2010. " Campbell and Rubin: A Primer and Comparison of Their Approaches to Causal Inference in Field Settings." Psychological Methods 15 (1): 3-17.
20. Shadish, William R., Thomas D. Cook, and Donald T. Campbell. 2002. Experimental and Quasi-Experimental Designs for Generalized Causal Inference . Belmont, CA : Wadsworth.
21. Titiunik, Roció. 2015. " Can Big Data Solve the Fundamental Problem of Causal Inference? " PS: Political Science and Politics 48 (1): this issue.