Talk to the Veterans Crisis Line now
U.S. flag
An official website of the United States government

VA Health Systems Research

Go to the VA ORD website
Go to the QUERI website

HSR Citation Abstract

Search | Search by Center | Search by Source | Keywords in Title

Predicting counterfactual risks under hypothetical treatment strategies: an application to HIV.

Dickerman BA, Dahabreh IJ, Cantos KV, Logan RW, Lodi S, Rentsch CT, Justice AC, HernĂ¡n MA. Predicting counterfactual risks under hypothetical treatment strategies: an application to HIV. European journal of epidemiology. 2022 Apr 1; 37(4):367-376.

Dimensions for VA is a web-based tool available to VA staff that enables detailed searches of published research and research projects.

If you have VA-Intranet access, click here for more information

VA staff not currently on the VA network can access Dimensions by registering for an account using their VA email address.
   Search Dimensions for VA for this citation
* Don't have VA-internal network access or a VA email address? Try searching the free-to-the-public version of Dimensions


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.

Questions about the HSR website? Email the Web Team

Any health information on this website is strictly for informational purposes and is not intended as medical advice. It should not be used to diagnose or treat any condition.