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Using causal forests to assess heterogeneity in cost‐effectiveness analysis

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  • Carl Bonander
  • Mikael Svensson

Abstract

We develop a method for data‐driven estimation and analysis of heterogeneity in cost‐effectiveness analyses (CEA) with experimental or observational individual‐level data. Our implementation uses causal forests and cross‐fitted augmented inverse probability weighted learning to estimate heterogeneity in incremental outcomes, costs and net monetary benefits, as well as other parameters relevant to CEA. We also show how the results can be visualized in relevant ways for the analysis of heterogeneity in CEA, such as using individual‐level cost effectiveness planes. Using a simulated dataset and an R package implementing our methods, we show how the approach can be used to estimate the average cost‐effectiveness in the entire sample or in subpopulations, explore and analyze the heterogeneity in incremental outcomes, costs and net monetary benefits (and their determinants), and learn policy rules from the data.

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  • Carl Bonander & Mikael Svensson, 2021. "Using causal forests to assess heterogeneity in cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 30(8), pages 1818-1832, August.
    • Handle: RePEc:wly:hlthec:v:30:y:2021:i:8:p:1818-1832
    DOI: 10.1002/hec.4263
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    2. David Glynn & John Giardina & Julia Hatamyar & Ankur Pandya & Marta Soares & Noemi Kreif, 2024. "Integrating decision modeling and machine learning to inform treatment stratification," Health Economics, John Wiley & Sons, Ltd., vol. 33(8), pages 1772-1792, August.

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