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New g-formula for the sequential causal effect and blip effect of treatment in sequential causal inference
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Mathematics. (Matematik)
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
2019 (English)In: Annals of Statistics, ISSN 0090-5364, E-ISSN 2168-8966Article in journal (Refereed) Accepted
Abstract [en]

In sequential causal inference, two types of causal effects are of practical interest, namely, the causal effect of the treatment regime (called the sequential causal effect) and the blip effect of treatmenton on the potential outcome after the last treatment. The well-known G-formula expresses these causal effects in terms of the standard paramaters. In this article, we obtain a new G-formula that expresses these causal effects in terms of the point observable effects of treatments similar to treatment in the framework of single-point causal inference. Based on the new G-formula, we estimate these causal effects by maximum likelihood via point observable effects with methods extended from single-point causal inference. We are able to increase precision of the estimation without introducing biases by an unsaturated model imposing constraints on the point observable effects. We are also able to reduce the number of point observable effects in the estimation by treatment assignment conditions.

Place, publisher, year, edition, pages
2019.
Keywords [en]
blip effect, curse of dimensionality, new G-formula, null paradox, point observable effect, sequential causal effect
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:hig:diva-29358OAI: oai:DiVA.org:hig-29358DiVA, id: diva2:1294432
Available from: 2019-03-07 Created: 2019-03-07 Last updated: 2019-03-07Bibliographically approved

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