Do-search -- a tool for causal inference and study design with multiple data sources
| Autor: | Juha Karvanen, Antti Hyttinen, Santtu Tikka |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Epidemiology Computer science media_common.quotation_subject Information Storage and Retrieval Machine learning computer.software_genre 01 natural sciences Statistics - Applications Methodology (stat.ME) 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Humans Applications (stat.AP) 030212 general & internal medicine 0101 mathematics Salt intake Statistics - Methodology media_common Selection bias business.industry Nutrition Surveys Missing data Causality Research Design Causal inference Meta-analysis Survey data collection Identifiability Artificial intelligence business computer |
| DOI: | 10.48550/arxiv.2007.08189 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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