Multiple imputation and test-wise deletion for causal discovery with incomplete cohort data.
Causal discovery algorithms estimate causal graphs from observational data. This can provide a valuable complement to analyses focusing on the causal relation between individual treatment-outcome pairs. Constraint-based causal discovery algorithms rely on conditional independence testing when buildi...
| Published in: | Statistics in Medicine Vol. 41; no. 23; pp. 4716 - 4744 |
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| Main Authors: | , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
Oct2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159193679&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159193679 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02776715 2DZ jtl: Statistics in Medicine issn: 02776715 maglogo: Y pubinfo: dt: Oct2022 vid: 41 iid: 23 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 159193679 158256703 159193679 NLM35908775 159193679 10.1002/sim.9535 NLM35908775 159193679 ppf: 4716 ppct: 28 formats: tig: atl: Multiple imputation and test-wise deletion for causal discovery with incomplete cohort data. aug: au: Witte, Janine Foraita, Ronja Didelez, Vanessa affil: Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany sug: subj: Study Design Algorithms Causal Attribution Child Prospective Studies Funding Source Human Child: 6-12 years ab: Causal discovery algorithms estimate causal graphs from observational data. This can provide a valuable complement to analyses focusing on the causal relation between individual treatment-outcome pairs. Constraint-based causal discovery algorithms rely on conditional independence testing when building the graph. Until recently, these algorithms have been unable to handle missing values. In this article, we investigate two alternative solutions: test-wise deletion and multiple imputation. We establish necessary and sufficient conditions for the recoverability of causal structures under test-wise deletion, and argue that multiple imputation is more challenging in the context of causal discovery than for estimation. We conduct an extensive comparison by simulating from benchmark causal graphs: as one might expect, we find that test-wise deletion and multiple imputation both clearly outperform list-wise deletion and single imputation. Crucially, our results further suggest that multiple imputation is especially useful in settings with a small number of either Gaussian or discrete variables, but when the dataset contains a mix of both neither method is uniformly best. The methods we compare include random forest imputation and a hybrid procedure combining test-wise deletion and multiple imputation. An application to data from the IDEFICS cohort study on diet- and lifestyle-related diseases in European children serves as an illustrating example. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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