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Identifying and estimating net effects of treatments in sequential casual inference
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences. (Matematik)
Department of Medical Epidemiology and Biostatistics, Karolinska Institute, .
2015 (English)In: Electronic Journal of Statistics, ISSN 1935-7524, E-ISSN 1935-7524, Vol. 9, 1608-1643 p.Article in journal (Refereed) Published
Abstract [en]

Suppose that a sequence of treatments are assigned to influence an outcome of interest that occurs after the last treatment. Between treatments, there are time-dependent covariates that may be post-treatment variables of the earlier treatments and confounders of the subsequent treatments. In this article, we study identification and estimation of the net effect of each treatment in the treatment sequence. We construct a point parametrization for the joint distribution of treatments, time-dependent covariates and the outcome, in which the point parameters of interest are the point effects of treatments considered as single-point treatments. We identify net effects of treatments by their expressions in terms of point effects of treatments and express patterns of net effects of treatments by constraints on point effects of treatments. We estimate net effects of treatments through their point effects under the constraint by maximum likelihood and reduce the number of point parameters in the estimation by the treatment assignment condition. As a result, we obtain an unbiased consistent maximum-likelihood estimate for the net effect of treatment even in a long treatment sequence. We also show by simulation that the interval estimation of the net effect of treatment achieves the nominal coverage probability.

Place, publisher, year, edition, pages
2015. Vol. 9, 1608-1643 p.
Keyword [en]
Net effect of treatment, pattern of net effects of treatments, point effect of treatment, constraint on point effects of treatments, treatment assignment condition, sequential causal inference
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:hig:diva-18759DOI: 10.1214/15-EJS1046ISI: 000366268800057ScopusID: 2-s2.0-84982672504OAI: oai:DiVA.org:hig-18759DiVA: diva2:780672
Available from: 2015-01-14 Created: 2015-01-14 Last updated: 2016-09-09Bibliographically approved

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Wang, Xiaoqin
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