Academic Journal
Causal Inference in the Presence of Missing Outcome and Treatment Variables: Triply Robust Estimator and Sensitivity Analysis.
| Τίτλος: | Causal Inference in the Presence of Missing Outcome and Treatment Variables: Triply Robust Estimator and Sensitivity Analysis. |
|---|---|
| Συγγραφείς: | Sim H; Department of Statistics, Jeonbuk National University, Jeonju, Jeonbuk State, Republic of Korea., Lee WK; Department of Emergency Medicine, Veterans Health Service Medical Center, Seoul, Republic of Korea., Lange C; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA., Lee W; Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea.; Institute of Health and Environment, Seoul National University, Seoul, Republic of Korea. |
| Πηγή: | Statistics in medicine [Stat Med] 2026 Jun; Vol. 45 (13-14), pp. e70630. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Chichester ; New York : Wiley, c1982- |
| Ιατρικοί όροι (MeSH): | Observational Studies as Topic*/methods , Models, Statistical* , Causality*, Humans ; Bias ; Data Interpretation, Statistical ; Confounding Factors, Epidemiologic ; Computer Simulation |
| Περίληψη: | Estimating causal effects from observational studies with missing data typically requires the no unmeasured confounder (NUC) assumption and the missing at random (MAR) assumption. However, correctly specifying all relevant models is often challenging in practice, and model misspecification can lead to biased estimates. In this study, we develop a triply robust estimator for causal effects when the outcome or treatment variable is partially observed. The proposed estimator incorporates outcome, treatment, and missing-data models, and remains consistent as long as at least two of the three models are correctly specified. Although this approach provides robustness against model misspecification, its validity still depends on the NUC and MAR assumptions, which are untestable from the observed data alone and may be frequently violated in the presence of unmeasured confounding. To address this limitation, we introduce a novel sensitivity analysis framework to evaluate the potential impact of unmeasured confounding on causal effect estimates and demonstrate its practical usefulness through an application to real data. (© 2026 John Wiley & Sons Ltd.) |
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| Grant Information: | 2021R1A2C1014409 National Research Foundation of Korea; RS-2026-25491141 National Research Foundation of Korea |
| Contributed Indexing: | Keywords: bootstrap; missing data; sensitivity analysis; triply robust estimation; unmeasured confounding |
| Entry Date(s): | Date Created: 20260609 Date Completed: 20260612 Latest Revision: 20260612 |
| Update Code: | 20260613 |
| DOI: | 10.1002/sim.70630 |
| PMID: | 42264550 |
| Βάση Δεδομένων: | MEDLINE |
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