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Attributable Effects and Full Matching for Binary Outcomes in Field Experiments and Observational Studies |
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Abstract:
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We introduce attributable effects and full matching to political scientists as a way to estimate treatment effects for binary dependent variables in field experiments and observational studies. |
Most Common Document Word Stems:
treatment (253), q (237), assign (163), vote (114), effect (110), match (106), person (97), 1 (88), control (85), test (83), ject (78), sub (77), differ (73), random (72), treat (71), covari (70), number (66), score (66), 2 (66), respons (64), one (64), |
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Association:
Name: The Midwest Political Science Association URL: http://www.indiana.edu/~mpsa/
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Citation:
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MLA Citation:
| Bowers, Jake. and Hansen, Ben. "Attributable Effects and Full Matching for Binary Outcomes in Field Experiments and Observational Studies" Paper presented at the annual meeting of the The Midwest Political Science Association, Palmer House Hilton, Chicago, Illinois, Apr 07, 2005 <Not Available>. 2008-10-10 <http://www.allacademic.com/meta/p84521_index.html> |
APA Citation:
| Bowers, J. W. and Hansen, B. , 2005-04-07 "Attributable Effects and Full Matching for Binary Outcomes in Field Experiments and Observational Studies" Paper presented at the annual meeting of the The Midwest Political Science Association, Palmer House Hilton, Chicago, Illinois Online <.PDF>. 2008-10-10 from http://www.allacademic.com/meta/p84521_index.html |
Publication Type: Conference Paper/Unpublished Manuscript Review Method: Peer Reviewed Abstract: We introduce attributable effects and full matching to political scientists as a way to estimate treatment effects for binary dependent variables in field experiments and observational studies. |
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| Document Type: |
.pdf |
| Page count: |
46 |
| Word count: |
21365 |
| Text sample: |
| Attributing Effects to a Get-Out-The-Vote Campaign Using Full Matching and Randomization Inference Jake Bowers and Ben Hansen Political Science and Statistics jwbowers@umich.edu and bbh@umich.edu University of Michigan April 4 2005 Abstract Statistical analysis requires a probability model: commonly a model for the dependence of outcomes Y on confounders X and a potentially causal variable Z . When the goal of the analysis is to infer Z 's effects on Y this requirement introduces an element of circularity: in order |
| on "Statistics and Causal Inference"." Journal of the American Statistical Association 81:961962. Rubin D.B. 1974. "Estimating the Causal Effects of Treatments in Randomized and Nonrandomized Studies." J. Educ. Psych. 66:688701. Somes Grant W. 1986. "The Generalized Mantel-Haenszel Statistic." The American Statistician 40:106108. Verba Sidney Kay L. Schlozman and Henry Brady. 1995. Voice and Equality: Civic Voluntarism in American Politics. Cambridge: Harvard University Press. Walter S. D. 1976. "The estimation and interpretation of attributable risk in health research." Biometrics 32:829849. |
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