Author's Keywords: randomization inference, experiments, field experiments, political participation, statistics, matching	Convention Submission, Review, and Scheduling! All Academic Convention can help with all of your abstract management needs and many more. Contact us today for a quote! Submission - Custom fields, multiple submission types, tracks, audio visual, multiple upload formats, automatic conversion to pdf. Review - Peer Review, Bulk reviewer assignment, bulk emails, ranking, z-score statistics, and multiple worksheets! Reports - Many standard and custom reports generated while you wait. Print programs with participant indexes, event grids, and more! Scheduling - Flexible and convenient grid scheduling within rooms and buildings. Conflict checking and advanced filtering. Communication - Bulk email tools to help your administrators send reminders and responses. Use form letters, a message center, and much more! Management - Search tools, duplicate people management, editing tools, submission transfers, many tools to manage a variety of conference management headaches! Click here for more information.
Association: Name: American Political Science Association URL: http://www.apsanet.org
Citation: URL: http://www.allacademic.com/meta/p40212_index.html Direct Link: HTML Code:

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 American Political Science Association, Marriott Wardman Park, Omni Shoreham, Washington Hilton, Washington, DC, Sep 01, 2005 <Not Available>. 2008-10-09 <http://www.allacademic.com/meta/p40212_index.html>

Bowers, J. and Hansen, B. , 2005-09-01 "Attributable Effects and Full Matching for Binary Outcomes in Field Experiments and Observational Studies" Paper presented at the annual meeting of the American Political Science Association, Marriott Wardman Park, Omni Shoreham, Washington Hilton, Washington, DC Online <APPLICATION/PDF>. 2008-10-09 from http://www.allacademic.com/meta/p40212_index.html

Publication Type: Conference Paper/Unpublished Manuscript Review Method: Peer Reviewed Abstract: Statistical analysis requires a probability model: commonly, a model for the dependence of outcomes $Y$ on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 to decide how $Z$ affects $Y$, the analyst first determines, speculatively, the manner of $Y$'s dependence on $Z$ and other variables. This paper takes a statistical perspective that avoids such circles, permitting analysis of $Z$'s effects on $Y$ even as the statistician remains entirely agnostic about the conditional distribution of $Y$ given $X$ and $Z$, or perhaps even denies that such a distribution exists. Our assumptions instead pertain to the conditional distribution $Z \vert X$, and the role of speculation in settling them is reduced by the existence of random assignment of $Z$ in a field experiment as well as by poststratification, testing for overt bias before accepting a poststratification, and optimal full matching. Such beginnings pave the way for ``randomization inference'', an approach which, despite a long history in the analysis of designed experiments, is relatively new to political science and to other fields in which experimental data are rarely available. The approach applies to both experiments and observational studies. We illustrate this by applying it to analyze A. Gerber and D. Green's New Haven Vote 98 campaign. Conceived as both a get-out-the-vote campaign and a field experiment in political participation, the study assigned households to treatment and desired to estimate the effect of treatment on the individuals nested within the households. We estimate the number of voters who would not have voted had the campaign not prompted them to --- that is, the total number of votes attributable to the interventions of the campaigners --- while taking into account the non-independence of observations within households, non-random compliance, and missing responses. Both our statistical inferences about these attributable effects and the stratification and matching that precede them rely on quite recent developments from statistics; our matching, in particular, has novel features of potentially wide applicability. Our broad findings resemble those of the original analysis by \citet{gerbergreen00}.

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