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