Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-22T19:03:22.066Z Has data issue: false hasContentIssue false
  • English
  • Français

All Else Equal in Theory and Data (Big or Small)

Published online by Cambridge University Press:  31 December 2014

Scott Ashworth ,
Christopher R. Berry  and
Ethan Bueno de Mesquita
Scott Ashworth
Affiliation:
University of Chicago
Christopher R. Berry
Affiliation:
University of Chicago
Ethan Bueno de Mesquita
Affiliation:
University of Chicago
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. 89 - 94
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

Anzia, Sarah F., and Berry, Christopher R.. 2011. “The Jackie (and Jill) Robinson Effect: Why Do Congresswomen Outperform Congressmen?” American Journal of Political Science 55 (3): 478–93.Google Scholar
Ashworth, Scott. 2006. “Campaign Finance and Voter Welfare with Entrenched Incumbents.” American Political Science Review 100 (1): 55–68.CrossRefGoogle Scholar
Belloni, Alexandre, Chernozhukov, Victor, and Hansen, Christian. 2014. “Inference on Treatment Effects after Selection among High-Dimensional Controls.” The Review of Economic Studies 81 (2): 608–50.Google Scholar
“Big Data in Political Science” Political Analysis Virtual Issue, http://www.oxfordjournals.org/our_journals/polana/virtualissue4.html.Google Scholar
Cox, Michael, and Ellsworth, David. 1997. “Application-Controlled Demand Paging for Out-of-Core Visualization.” In Proceedings of the 8th Conference on Visualization ’97 (VIS ’97). 235–ff. Los Alamitos, CA: IEEE Computer Society Press. Available athttp://dl.acm.org/citation.cfm?id=266989.267068.Google Scholar
Gentzkow, Matthew, and Shapiro, Jesse M.. 2010. “What Drives Media Slant? Evidence from US Daily Newspapers.” Econometrica 78 (1): 35–71.Google Scholar
Gerber, Alan, and Malhotra, Neil. 2008. “Do Statistical Reporting Standards Affect What Is Published? Publication Bias in Two Leading Political Science Journals.” Quarterly Journal of Political Science 3 (3): 313–26.Google Scholar
Giere, Ronald N. 1988. Explaining Science: A Cognitive Approach. Chicago: University of Chicago Press.Google Scholar
Grimmer, Justin. 2010. “A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases.” Political Analysis 18 (1): 1–35.Google Scholar
Hacking, Ian. 1983. Representing and Intervening: Introductory Topics in the Philosophy of Natural Science. New York: Cambridge University Press.CrossRefGoogle Scholar
Hastie, Trevor, Tibshirani, Robert, and Friedman, Jerome. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, second edition. New York: Springer-Verlag.Google Scholar
Holland, Paul W. 1986. “Statistics and Causal Inference.” Journal of the American Statistical Association 81 (396): 945–60.Google Scholar
Houser, Daniel, and Stratmann, Thomas. 2008. “Selling Favors in the Lab: Experiments on Campaign Finance Reform.” Public Choice 136 (1–2): 215–39.Google Scholar
Lucas, Robert E. Jr. 1976. “Econometric Policy Evaluation: A Critique.” In The Phillips Curve and Labor Markets, Brunner, Karl and Meltzer, Alan H. (eds). Carnegie-Rochester Conference Series on Public Policy, Volume 1, 19–46. Amsterdam: North-Holland.Google Scholar
Manski, Charles F. 2007. Identification for Prediction and Decision. Cambridge, MA: Harvard University Press.Google Scholar
Morgan, Mary. 2012. The World in the Model: How Economists Work and Think. New York: Cambridge University Press.Google Scholar
National Research Council. 2013. Frontiers in Massive Data Analysis. Washington, DC: The National Academies Press.Google Scholar
Prat, Andrea. 2002. “Campaign Advertising and Voter Welfare.” Review of Economic Studies 69 (4): 997–1017.Google Scholar
Rosenbaum, Paul R. 2010. Design of Observational Studies. New York: Springer.CrossRefGoogle ScholarPubMed
Tillmann, Philipp. 2014. “Entry into Electoral Races and the Quality of Representation.” University of Chicago manuscript.Google Scholar
Titiunik, Rocío. 2015. “Can Big Data Solve the Fundamental Problem of Causal Inference?” PS: Political Science and Politics 48 (1): this issue.Google Scholar