A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Karvanen, Juha; Tikka, Santtu; Hyttinen, Antti

Lehti tai sarja: Epidemiology

ISSN: 1044-3983

eISSN: 1531-5487

Julkaisuvuosi: 2021

Volyymi: 32

Lehden numero: 1

Artikkelin sivunumerot: 111-119

Kustantaja: Wolters Kluwer

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus:

Rinnakkaistallenteen verkko-osoite (pre-print): https://arxiv.org/abs/2007.08189


Tiivistelmä

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.


YSO-asiasanat: epidemiologia; meta-analyysi; tilastomenetelmät; kausaliteetti; algoritmit


Liittyvät organisaatiot


OKM-raportointi: Kyllä

Raportointivuosi: 2021

Alustava JUFO-taso: 3


Viimeisin päivitys 2021-07-07 klo 17:56