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Activity Number: 220
Type: Topic Contributed
Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
Sponsor: Health Policy Statistics Section
Abstract #311286
Title: Instrumental Variables Estimation with Some Invalid Instruments and Its Application to Mendelian Randomization
Author(s): Hyunseung Kang*+ and Anru Zhang and Tony Cai and Dylan Small
Companies: University of Pennsylvania and University of Pennsylvania and University of Pennsylvania and University of Pennsylvania
Keywords: causal inference ; instrumental variables ; L1 penalization ; Mendelian randomization
Abstract:

Instrumental variables have been widely used for estimating the causal effect between exposure and outcome. Conventional estimation methods require complete knowledge about all the instruments' validity; a valid instrument must not have a direct effect on the outcome and not be related to unmeasured confounders. Often, this is impractical as highlighted by Mendelian randomization (MR) studies where genetic markers are used as instruments and complete knowledge about instruments' validity is equivalent to complete knowledge about the involved genes' functions. In this paper, we propose a method for estimation of causal effects when this complete knowledge is absent. It is shown that identification and estimation is possible under a weaker requirement that more than 50% of instruments are valid, without precisely knowing which of the 50%+ instruments are valid. A fast penalized L1 estimation method, called sisVIVE, is introduced for estimating the causal effect without knowing which instruments are valid, with theoretical results. The proposed method is demonstrated on simulated data and a real MR study concerning the effect of body mass index on health-related quality of life index.


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