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Abstract Details
Activity Number:
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524
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract - #303424 |
Title:
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Efficient Targeted Estimation Using Instrumental Variables
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Author(s):
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Boriska Toth*+ and Mark van der Laan
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Companies:
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University of California at Berkeley and University of California at Berkeley
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Address:
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1540 Milvia St. #6, Berkeley, CA, 94709, United States
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Keywords:
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semiparametric estimation ;
causal inference ;
clinical trials ;
instrumental variables ;
targeted maximum likelihood ;
treatment effect
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Abstract:
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The method of instrumental variables can be used to obtain an unbiased estimate of a causal effect in the presence of unmeasured confounding between a treatment and outcome. We shed light on the limitations and potential of this method by using a novel estimator that is, in some sense, optimal. We use a targeted maximum likelihood estimator (TMLE), which is semiparametric, asymptotically efficient, and a substitution estimator. We derive a TMLE estimator for the causal effect of treatment, as well as the assumptions needed for unbiased estimation and identifiability. A crucial question concerning the use of instrumental variables is whether the gain in bias reduction compensates the blowup in variance as compared to using a biased estimator that doesn't account for unmeasured confounding. We answer this question both analytically and empirically. We find that in regions of high unmeasured confounding and a strong instrument, the instrumental variable-based estimator is superior. We further compare the TMLE estimator to other major approaches to estimating the causal effect, concluding that TMLE performs favorably.
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