A1 Journal article (refereed)
Do-search : A Tool for Causal Inference and Study Design with Multiple Data Sources (2021)

Karvanen, J., Tikka, S., & Hyttinen, A. (2021). Do-search : A Tool for Causal Inference and Study Design with Multiple Data Sources. Epidemiology, 32(1), 111-119. https://doi.org/10.1097/EDE.0000000000001270

JYU authors or editors

Publication details

All authors or editors: Karvanen, Juha; Tikka, Santtu; Hyttinen, Antti

Journal or series: Epidemiology

ISSN: 1044-3983

eISSN: 1531-5487

Publication year: 2021

Volume: 32

Issue number: 1

Pages range: 111-119

Publisher: Wolters Kluwer

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1097/EDE.0000000000001270

Publication open access: Not open

Publication channel open access:

Web address of parallel published publication (pre-print): https://arxiv.org/abs/2007.08189

Abstract

Epidemiologic evidence is based on multiple data sources including clinical trials, cohort studies, surveys, registries, and expert opinions. Merging information from different sources opens up new possibilities for the estimation of causal effects. We show how causal effects can be identified and estimated by combining experiments and observations in real and realistic scenarios. As a new tool, we present do-search, a recently developed algorithmic approach that can determine the identifiability of a causal effect. The approach is based on do-calculus, and it can utilize data with nontrivial missing data and selection bias mechanisms. When the effect is identifiable, do-search outputs an identifying formula on which numerical estimation can be based. When the effect is not identifiable, we can use do-search to recognize additional data sources and assumptions that would make the effect identifiable. Throughout the article, we consider the effect of salt-adding behavior on blood pressure mediated by the salt intake as an example. The identifiability of this effect is resolved in various scenarios with different assumptions on confounding. There are scenarios where the causal effect is identifiable from a chain of experiments but not from survey data, as well as scenarios where the opposite is true. As an illustration, we use survey data from the National Health and Nutrition Examination Survey 2013–2016 and the results from a meta-analysis of randomized controlled trials and estimate the reduction in average systolic blood pressure under an intervention where the use of table salt is discontinued.

Keywords: epidemiology; meta-analysis; statistical methods; causality; algorithms

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2021

JUFO rating: 3