Identification of Causal Effects on Binary Outcomes Using Structural Mean Models

Paul Clarke and Frank Windmeijer

The Centre for Market and Public Organisation from The Centre for Market and Public Organisation, University of Bristol, UK

Abstract: Structural mean models (SMMs) are used to estimate causal effects among those selecting treatment in randomised controlled trials affected by non-ignorable non-compliance. These causal effects can be identified by assuming that there is no effect modification, namely, that the causal effect is equal for the treated subgroups randomised to treatment and to control. By analysing simple structural models for binary outcomes, we argue that the no effect modification assumption does not hold in general, and so SMMs do not estimate causal effects for the treated. An exception is for designs in which those randomised to control can be completely excluded from receiving the treatment. However, when there is non-compliance in the control arm, local (or complier) causal effects can be identified provided that the further assumption of monotonic selection into treatment holds. We demonstrate these issues using numerical examples.

Keywords: structural mean models; identification; local average treatment effects; complier average treatment effects (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Pages: 20 pages
Date: 2009-06
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Working Paper: Identification of causal effects on binary outcomes using structural mean models (2010)
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