ArticleInternational journal of epidemiology2015
Imputation approaches for potential outcomes in causal inference.
Article in International journal of epidemiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Influence of Fitness Apps on Sports Habits, Satisfaction, and Intentions to Stay in Fitness Center Users: An Experimental Study.International journal of environmental research and public health · 2021Trial
- Using inverse probability of censoring weighting to estimate hypothetical estimands in clinical trials: Should we implement stabilisation, and if so how?Statistical methods in medical research · 2026Article
- Estimating the effect of pre-exposure prophylaxis in Black men who have sex with men.International journal of epidemiology · 2024Article
- Implementing multiple imputations for addressing missing data in multireader multicase design studies.BMC medical research methodology · 2024Article
- Estimating the Effect of PrEP in Black Men Who Have Sex with Men: A Framework to Utilize Data from Multiple Non-Randomized Studies to Estimate Causal Effects.medRxiv : the preprint server for health sciences · 2024Article
- Model-based standardization using multiple imputation.BMC medical research methodology · 2024Article
- FIVE AUTHORS REPLY.American journal of epidemiology · 2024Article
- Study design approaches for future active-controlled HIV prevention trials.Statistical communications in infectious diseases · 2024Article
- Long-term causal effects of far-right terrorism in New Zealand.PNAS nexus · 2023Article
- Inverse Probability Weighting to Estimate Exposure Effects on the Burden of Recurrent Outcomes in the Presence of Competing Events.American journal of epidemiology · 2023Article
- Missing Outcome Data in Epidemiologic Studies.American journal of epidemiology · 2023Article
- Hypothetical Estimands in Clinical Trials: A Unification of Causal Inference and Missing Data Methods.Statistics in biopharmaceutical research · 2023Article
- Multiple imputation procedures for estimating causal effects with multiple treatments with application to the comparison of healthcare providers.Statistics in medicine · 2022Article
- Characteristics, Prognosis, and Competing Risk Nomograms of Cutaneous Malignant Melanoma: Evidence for Pigmentary Disorders.Frontiers in oncology · 2022Article
- Estimating the optimal timing of surgery by imputing potential outcomes.Statistics in medicine · 2021Article
- [Causal relationship between physical exercise and risk of ischemic stroke recurrence based on the potential outcome theory].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2021Article
- Multiple imputation to quantify misclassification in observational studies of the cognitively impaired: an application for pain assessment in nursing home residents.BMC medical research methodology · 2021Article
- Reflection on modern methods: when worlds collide-prediction, machine learning and causal inference.International journal of epidemiology · 2021Article
- Doubly robust estimator of risk in the presence of censoring dependent on time-varying covariates: application to a primary prevention trial for coronary events with pravastatin.BMC medical research methodology · 2020Article
- Effectiveness of Potential Interventions to Change Gendered Social Norms on Prevalence of Intimate Partner Violence in Uganda: a Causal Inference Approach.Prevention science : the official journal of the Society for Prevention Research · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundThe fundamental problem of causal inference is one of missing data, and specifically of missing potential outcomes: if potential outcomes were fully observed, then causal inference could be made trivially. Though often not discussed explicitly in the epidemiological literature, the connections between causal inference and missing data can provide additional intuition.
methodsWe demonstrate how we can approach causal inference in ways similar to how we address all problems of missing data, using multiple imputation and the parametric g-formula.
resultsWe explain and demonstrate the use of these methods in example data, and discuss implications for more traditional approaches to causal inference.
conclusionsThough there are advantages and disadvantages to both multiple imputation and g-formula approaches, epidemiologists can benefit from thinking about their causal inference problems as problems of missing data, as such perspectives may lend new and clarifying insights to their analyses.
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