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Abstract Details
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Activity Number:
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304
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #305391 |
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Title:
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An Unidentifiability Issue in G-Estimation and Suggestions to Fix It
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Author(s):
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Yang Jiang*+
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Companies:
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The Wharton School
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Address:
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329 S. 42nd Street, Philadelphia, PA, 19104, United States
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Keywords:
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g-estimation ;
counterfactual outcome ;
unidentifiability ;
future ignorability assumption
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Abstract:
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In causal inference for longitudinal data, g-estimation gives a consistent estimator of the treatment effect under the sequential randomization assumption. Often, g-estimation is used along with a parameterized potential outcome model and a logistic model for the propensity score. However, the mle may not exist for some extreme treatment effect we plug in the model and this leads to some unidentifiability issues. We investigate and compare several methods for fixing this problem. Then we extend the discussion to the case of multiple treatments and relaxed assumptions (future ignorability assumption instead of sequential randomization assumption).
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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