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Abstract Details
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Activity Number:
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457
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Social Statistics Section
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| Abstract - #305308 |
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Title:
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Sensitivity Analysis for Multilevel Data
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Author(s):
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Jennifer Hill*+ and Marc Scott and Nicole Carnegie
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Companies:
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NYU Steinhardt and NYU and NYU
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Address:
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HMSS, New York, NY, 10012, United States
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Keywords:
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sensitivity analysis ;
unobserved confounder ;
omitted variable bias ;
multilevel models
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Abstract:
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Strategies to assess the sensitivity of causal inferences to violations of the ignorability assumption due to failure to control for key confounders have received increasing attention over the last decade. This paper uses work by Imbens and others that model the relationship of an unobserved confounder to both the response surface and the treatment assignment mechanism as a building block and then extends the framework to accommodate grouped data structures. Among other complications these extensions will address how to best characterize the contribution of either an individual or group-level covariate to both linear and non-linear multilevel models.
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