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Abstract Details
Activity Number:
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648
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Health Policy Statistics
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Abstract - #301488 |
Title:
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Support Vector Machines as a Matching Method to Achieve Optimal Balance
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Author(s):
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Marc Ratkovic*+
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Companies:
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Princeton University
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Address:
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, , ,
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Keywords:
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propensity score ;
balance ;
support vector machines
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Abstract:
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Existing matching methods match on some summary statistic-being in the same bin, or close in some metric (Mahalanobis, propensity score). This necessarily requires a compression of information and ad hoc decisions by the researcher. In this paper, I generate a method that simultaneously matches across all marginals, rather than a summary statistic. This allows for a selection of observations that creates a distribution across covariates that is independent of assignment to treatment. I do this through adapting support vector machines to the matching problem. I prove that observations with non-zero slack are balanced, in expectation, across all covariates. Through the kernel trick, I am able to extend the results to independence between treatment assignment and covariates, rather than simple uncorrelatedness. A comparison to extant methods reveals a far better balance.
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Authors who are presenting talks have a * after their name.
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