ArticleEuropean journal of epidemiology2022
Predicting counterfactual risks under hypothetical treatment strategies: an application to HIV.
Article in European journal of epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Delayed versus primary closure to minimize risk of surgical-site infection for complicated appendicitis: A secondary analysis of a randomized trial using counterfactual prediction modeling.Infection control and hospital epidemiology · 2024Trial
- Predicting disease progression under a "treatment-naive" counterfactual scenario in a competing risks framework.Frontiers in epidemiology · 2026Article
- Counterfactual prediction from machine learning models: transportability and joint analysis for model development and evaluation using multi-source data.Diagnostic and prognostic research · 2025Article
- Estimating and Evaluating Counterfactual Prediction Models.Statistics in medicine · 2025Article
- Individualized Effects of Weight Gain in Adulthood on the Development of MASLD in Japanese Non-Obese Individuals.Journal of gastroenterology and hepatology · 2025Article
- Risk-Based Decision Making: Estimands for Sequential Prediction Under Interventions.Biometrical journal. Biometrische Zeitschrift · 2024Article
- Article
- Assessing the transportability of clinical prediction models for cognitive impairment using causal models.BMC medical research methodology · 2023Article
- Cohort profile: the Turin prostate cancer prognostication (TPCP) cohort.Frontiers in oncology · 2023Article
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Authors and funding
8 authors.
Funding
Abstract
The accuracy of a prediction algorithm depends on contextual factors that may vary across deployment settings. To address this inherent limitation of prediction, we propose an approach to counterfactual prediction based on the g-formula to predict risk across populations that differ in their distribution of treatment strategies. We apply this to predict 5-year risk of mortality among persons receiving care for HIV in the U.S. Veterans Health Administration under different hypothetical treatment strategies. First, we implement a conventional approach to develop a prediction algorithm in the observed data and show how the algorithm may fail when transported to new populations with different treatment strategies. Second, we generate counterfactual data under different treatment strategies and use it to assess the robustness of the original algorithm's performance to these differences and to develop counterfactual prediction algorithms. We discuss how estimating counterfactual risks under a particular treatment strategy is more challenging than conventional prediction as it requires the same data, methods, and unverifiable assumptions as causal inference. However, this may be required when the alternative assumption of constant treatment patterns across deployment settings is unlikely to hold and new data is not yet available to retrain the algorithm.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.