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Abstract Details

Activity Number: 392
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #304761
Title: Making Instrumental Variables Look More Like Experimental Design
Author(s): Mike Baiocchi*+ and Dylan Small and Daniel Polsky
Companies: Stanford University and The Wharton School and University of Pennsylvania
Address: Department of Statistics, Stanford, CA, , United States
Keywords: instrumental variables ; causal inference ; near/far matching ; study design ; propensity score ; matching

Instrumental variable (IV) techniques are typically conceptualized and implemented under a structural equation modelling framework(e.g., two stage least squares). This is not the only framework for IV. A study design approach to IV has been developed. This technique, called near/far matching, follows the logic of a randomized controlled trial to obtain causal estimates in an observational setting.

We will start by introducing near/far matching and discussing its connection to propensity score matching. We will then introduce a methodology for designing a strong study using near/far matching, both when you believe you have a valid instrument and when you suspect a violation of the IV assumptions.

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