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Activity Number: 652
Type: Contributed
Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #306859
Title: TMLE for Marginal Structural Models Based on Instrument
Author(s): Boriska Toth*+ and Mark van der Laan
Companies: and University of California at Berkeley School of Public Health
Address: 1540 Milvia St., Berkeley, CA, 94709, United States
Keywords: instrumental variables ; causal inference ; confounding ; non-parametric ; efficiency theory ; biostatistics

We consider estimation of a causal effect of a possibly continuous treatment when treatment assignment is potentially subject to unmeasured confounding, but an instrument is available. Our semiparametric structural equation for the outcome as a function of treatment and covariates assumes that the effect of treatment is linear, conditional on the observed baseline covariates. This weakens the commonly made linearity assumption. The structural equation also assumes that the conditional mean of its error, given the instrument and baseline covariates, equals zero, which is the typical instrumental variable assumption. We establish identifiability of marginal causal effects of the treatment as defined by projections of the true causal dose-response curve onto a user supplied working marginal structural model. We derive the efficient influence curve of the resulting statistical parameter/e

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