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Can Big Data Solve the Fundamental Problem of Causal Inference?

Published online by Cambridge University Press:  31 December 2014

Rocío Titiunik
Rocío Titiunik*
Affiliation:
University of Michigan
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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. 75 - 79
Copyright
Copyright © American Political Science Association 2015 

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References

REFERENCES

Andrews, Donald W. K., and Stock, James H.. 2007. “Inference with Weak Instruments.” In Advances in Economics and Econometrics: Theory and Applications, Ninth World Congress, Vol. III, ed. Blundell, Richard, Newey, Whitney K. and Persson, Torsten, 122–73. New York: Cambridge University Press.Google Scholar
Belloni, Alexandre, Chernozhukov, Victor, Fernández-Val, Iván, and Hansen, Christian. 2013. “Program Evaluation with High-Dimensional Data.” Available at arXiv preprint arXiv:1311.2645.Google Scholar
Belloni, Alexandre, Chernozhukov, Victor, and Hansen, Christian. 2014. “High-Dimensional Methods and Inference on Structural and Treatment Effects.” Journal of Economic Perspectives 28 (2): 29–50.Google Scholar
Berk, Richard, Brown, Lawrence, Buja, Andreas, Zhang, Kai, Zhao, Linda, et al. 2013. “Valid Post-Selection Inference.” The Annals of Statistics 41 (2): 802–37.Google Scholar
Cattaneo, Matias D., Jansson, Michael, and Newey, Whitney K.. 2012. “Alternative Asymptotics and the Partially Linear Model with Many Regressors.” Working paper. Ann Arbor: University of Michigan.Google Scholar
Efron, Bradley. 2010. Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction, Volume 1. Cambridge: Cambridge University Press.Google Scholar
Farrell, Max H. 2014. “Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations.” Working paper. Chicago: University of Chicago Press.Google Scholar
Gerber, Alan S., and Green, Donald P.. 2012. Field Experiments: Design, Analysis, and Interpretation. New York: W. W. Norton.Google Scholar
Grimmer, Justin. 2013. “Appropriators Not Position Takers: The Distorting Effects of Electoral Incentives on Congressional Representation.” American Journal of Political Science 57 (3): 624–42.CrossRefGoogle Scholar
Hahn, Jinyong, and Newey, Whitney. 2004. “Jackknife and Analytical Bias Reduction for Nonlinear Panel Models.” Econometrica 72 (4): 1295–319.Google Scholar
Hastie, Trevor, Tibshirani, Robert, and Friedman, Jerome. 2009. The Elements of Statistical Learning, second edition. New York: Springer.CrossRefGoogle Scholar
Heckman, James J., and Vytlacil, Edward J.. 2007. “Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation.” Handbook of Econometrics 6:4779–874.Google Scholar
Holland, Paul W. 1986. “Statistics and Causal Inference.” Journal of the American Statistical Association 81 (396): 945–60.Google Scholar
Imbens, Guido W., and Wooldridge, Jeffrey M.. 2009. “Recent Developments in the Econometrics of Program Evaluation.” Journal of Economic Literature 47 (1): 5–86.Google Scholar
Koenker, Roger. 1988. “Asymptotic Theory and Econometric Practice.” Journal of Applied Econometrics 3 (2): 139–47.Google Scholar
Lazer, David, Kennedy, Ryan, King, Gary, and Vespignani, Alessandro. 2014. “The Parable of Google Flu: Traps in Big Data Analysis.” Science 343 (6176): 1203–5.Google Scholar
Leamer, Edward E. 1978. Specification Searches: Ad Hoc Inference with Nonexperimental Data. New York:Wiley.Google Scholar
Leeb, Hannes, and Pötscher, Benedikt M.. 2005. “Model Selection and Inference: Facts and Fiction.” Econometric Theory 21 (01): 21–59.Google Scholar
Michalski, Ryszard S., Carbonell, Jaime G., and Mitchell, Tom M.. 1983. “An Overview of Machine Learning.” In Machine Learning: An Artificial Intelligence Approach, ed. Michalski, Ryszard S., Carbonell, Jaime G., and Mitchell, Tom M., 3–23. Palo Alto, CA: Tioga Publishing Co.Google Scholar
Pearl, Judea. 2009. Causality: Models, Reasoning and Inference, Volume 29, second edition. Cambridge: Cambridge University Press.Google Scholar
Rosenbaum, Paul R. 2010. Design of Observational Studies. New York: Springer.Google Scholar
Rubin, Donald B. 2005. “Causal Inference Using Potential Outcomes.” Journal of the American Statistical Association 100 (469): 322–31.Google Scholar
Sekhon, Jasjeet S. 2009. “Opiates for the Matches: Matching Methods for Causal Inference.” Annual Review of Political Science 12: 487–508.Google Scholar
Stock, James H., and Watson, Mark W.. 2008. “Heteroskedasticity: Robust Standard Errors for Fixed Effects Panel Data Regression.” Econometrica 76 (1): 155–74.Google Scholar
Van der Laan, Mark J., and Robins, James M.. 2003. Unified Methods for Censored Longitudinal Data and Causality. New York: Springer.Google Scholar
Witten, Ian H., and Frank, Eiber. 2005. Data Mining: Practical Machine Learning Tools and Techniques, second edition. Burlington, MA: Morgan Kaufmann.Google Scholar