Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-20T03:44:05.540Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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