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We Are All Social Scientists Now: How Big Data, Machine Learning, and Causal Inference Work Together

Published online by Cambridge University Press:  31 December 2014

Justin Grimmer
Justin Grimmer*
Affiliation:
Stanford University
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Abstract

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Type
Symposium: Big Data, Causal Inference, and Formal Theory: Contradictory Trends in Political Science?
Information
PS: Political Science & Politics , Volume 48 , Issue 1 , January 2015 , pp. 80 - 83
Copyright
Copyright © American Political Science Association 2015 

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References

REFERENCES

Adler, E. Scott, and Wilkerson, John. 2014. “Congressional Bills Project.” Available atwww.congressionalbills.org. Accessed August 1, 2014.Google Scholar
Bond, Robert, and Messing, Solomon. “Quantifying Social Media’s Political Space: Estimating Ideology from Publicly Revealed Preferences on Facebook.” Stanford University Unpublished Manuscript.Google Scholar
Bonica, Adam. 2014. “Mapping the Ideological Marketplace.” American Journal of Political Science 58 (2): 367–87.Google Scholar
Boydstun, Amber. 2013. Making the News: Politics, the Media, and Agenda Setting. Chicago: University of Chicago Press.Google Scholar
Chang, Jonathan, Boyd-Graber, Jordan, Gerrish, Sean, Wang, Chong, and Blei, David. 2009. “Reading Tea Leaves: How Humans Interpret Topic Models.” In Neural Information Processing Systems Proceedings, 288–96.Google Scholar
Clinton, Joshua D., and Jackman, Simon. 2009. “To Simulate or NOMINATE?” Legislative Studies Quarterly 34 (4): 593–621.CrossRefGoogle Scholar
Clinton, Joshua, Jackman, Simon, and Rivers, Douglas. 2004. “The Statistical Analysis of Roll Call Data.” American Political Science Review 98 (02): 355–70.Google Scholar
Davenport, Lauren. 2014. “Politics between Black and White.” Redwood City, CA: Stanford University Unpublished Manuscript.Google Scholar
Fowler, James H., Heaney, Michael T., Nickerson, David W., Padgett, John F., and Betsy, Sinclair. 2011. “Causality in Political Networks.” American Politics Research 2: 437–80.CrossRefGoogle Scholar
Gerring, John. 2012. “Mere Description.” British Journal of Political Science 42 (4): 721–46.Google Scholar
Green, Donald P., and Kern, Holger L.. 2012. “Modeling Heterogeneous Treatment Effects in Survey Experiments with Bayesian Additive Regression Trees.” Public Opinion Quarterly 76 (3): 491–511.Google Scholar
Grimmer, Justin, and Stewart, Brandon M.. 2013. “Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts.” Political Analysis 21 (3): 267– 97.Google Scholar
Hazlett, Chad. 2014. “Kernel Balancing (KBAL): A Balancing Method to Equalize Multivariate Distance Densities and Reduce Bias without a Specification Search.” Cambridge, MA: MIT Unpublished Manuscript.Google Scholar
Higgins, Michael J., and Sekhon, Jasjeet S.. 2014. “Improving Experiments by Optimal Blocking: Minimizing the Maximum Within-Block Distance.” Berkeley: University of California Unpublished Manuscript.Google Scholar
Ho, Dan, Imai, Kosuke, King, Gary, and Stuart, Elizabeth. 2007. “Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference.” Political Analysis 15 (3): 199–236.Google Scholar
Imai, Kosuke, King, Gary, and Stuart, Elizabeth. 2008. “Misunderstandings between Experimentalists and Observationalists about Causal Inference.” Journal of the Royal Statistical Society: Series A (Statistics in Society) 171 (2): 481–502.Google Scholar
Imai, Kosuke, and Ratkovic, Marc. 2013. “Estimating Treatment Effect Heterogeneity in Randomized Program Evaluation.” Annals of Applied Statistics 7 (1): 443–70.Google Scholar
Jones, Bryan, Wilkerson, John, and Baumgartner, Frank. 2009. “The Policy Agendas Project.” Available athttp://www.policyagendas.org. Accessed August 1, 2014.Google Scholar
Krehbiel, Keith. 1998. Pivotal Politics: A Theory of US Lawmaking. Chicago: University of Chicago Press.Google Scholar
Lee, David, Moretti, Enrico, and Butler, Matthew. 2004. “Do Voters Affect or Elect Policies? Evidence from the US House.” Quarterly Journal of Economics 119 (3): 807–59.Google Scholar
Lee, Frances. 2008. “Dividers, Not Uniters: Presidential Leadership and Senate Partisanship, 1981–2004.” Journal of Politics 70 (4): 914–28.CrossRefGoogle Scholar
McCarty, Nolan, Poole, Keith, and Rosenthal, Howard. 2006. Polarized America: The Dance of Inequality and Unequal Riches. Cambridge, MA: MIT Press.Google Scholar
McCarty, NolanKeith, PooleHoward, Rosenthal. 2009. “Does Gerrymandering Cause Polarization?” American Journal of Political Science 53 (3): 666–80.CrossRefGoogle Scholar
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.Google Scholar
Moore, Ryan T., and Moore, Sally A.. 2013. “Blocking for Sequential Political Experiments.” Political Analysis 21 (4): 507–23.Google Scholar
Nagler, Jonathan, and Tucker, Joshua. 2015. “Drawing Inferences and Testing Theories with Big Data.” PS: Political Science and Politics 48 (1): this issue.Google Scholar
Patty, John, 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. 1984. “Least Squares Metric, Unidimensional Unfolding.” Psychometrika 49 (3): 311–23.Google Scholar
Poole, Keith, and Rosenthal, Howard. 1984. “The Polarization of American Politics.” Journal of Politics 46 (4): 1061–79.Google Scholar
Poole, KeithHoward, Rosenthal. 1985. “A Spatial Model for Legislative Roll Call Analysis.” American Journal of Political Science 29 (2): 357–84.Google Scholar
Poole, KeithHoward, Rosenthal. 1997. Congress: A Political-Economic History of Roll Call Voting. Oxford: Oxford University Press.Google Scholar
Quinn, Kevinet al. 2010. “How to Analyze Political Attention with Minimal Assumptions and Costs.” American Journal of Political Science 54 (1): 209–27.CrossRefGoogle Scholar
Roberts, Margaret E.et al. 2014. “Structural Topic Models for Open-Ended Survey Responses.” American Journal of Political Science. DOI 10.1111/ajps.12103.Google Scholar
Rosenbaum, Paul R., and Rubin, Donald R.. 1983. “The Central Role of the Propensity Score in Observational Studies for Causal Effects.” Biometrika 70 (1): 41–55.Google Scholar
Sekhon, Jasjeet S. 2009. “Opiates for the Matches: Matching Methods for Causal Inference.” Annual Review of Political Science 12: 487–508.CrossRefGoogle Scholar
Theriault, Sean M. 2008. Party Polarization in Congress. Cambridge: Cambridge University Press.Google Scholar