MethodsDo-search A Tool for Causal Inference and Study Design with Multiple Data SourcesKarvanen, Juhaa; Tikka, Santtua; Hyttinen, AnttibAuthor Information From the aDepartment of Mathematics and Statistics, University of Jyvaskyla, Finland bHelsinki Institute for Information Technology, Department of Computer Science, University of Helsinki, Finland. Submitted November 21, 2019; accepted September 24, 2020 S.T. was supported by Academy of Finland grant 311877 (Decision analytics utilizing causal models and multiobjective optimization). A.H. was supported by Academy of Finland grant 295673. The data and the code will be available as Supplemental Digital Content. Disclosure: J.K. is a speaker fee from Biogen Finland. The other authors report no conflicts of interest. Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com). This content is not peer-reviewed or copy-edited; it is the sole responsibility of the authors. Correspondence: Juha Karvanen, Department of Mathematics and Statistics, University of Jyvaskyla, P.O.Box (MaD), FI-40014, Finland. E-mail: [email protected]. Epidemiology: January 2021 - Volume 32 - Issue 1 - p 111-119 doi: 10.1097/EDE.0000000000001270 Buy SDC Metrics 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. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.