On Partial Identification of the Natural Indirect Effect

Miles Caleb (), Kanki Phyllis, Meloni Seema and Tchetgen Tchetgen Eric
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Miles Caleb: Division of Biostatistics, University of California at Berkeley, Berkeley, CA, USA
Kanki Phyllis: Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
Meloni Seema: Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
Tchetgen Tchetgen Eric: Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

Journal of Causal Inference, 2017, vol. 5, issue 2, 12

Abstract: In causal mediation analysis, nonparametric identification of the natural indirect effect typically relies on, in addition to no unobserved pre-exposure confounding, fundamental assumptions of (i) so-called “cross-world-counterfactuals” independence and (ii) no exposure-induced confounding. When the mediator is binary, bounds for partial identification have been given when neither assumption is made, or alternatively when assuming only (ii). We extend existing bounds to the case of a polytomous mediator, and provide bounds for the case assuming only (i). We apply these bounds to data from the Harvard PEPFAR program in Nigeria, where we evaluate the extent to which the effects of antiretroviral therapy on virological failure are mediated by a patient’s adherence, and show that inference on this effect is somewhat sensitive to model assumptions.

Keywords: cross-world counterfactual; mediation; partial identification; single world intervention graph; natural indirect effect (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:5:y:2017:i:2:p:12:n:3

DOI: 10.1515/jci-2016-0004

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