Jonas Esser, Mateus Maia, Judith Bosmans, Johanna van Dongen
arXiv 4 Jul 2025 · Econometrics
arXiv:2507.03511 · PDF · DOI · OpenAlex · Extracted main text
Healthcare decision-making often requires selecting among treatment options under budget constraints, particularly when one option is more effective but also more costly. Cost-effectiveness analysis (CEA) provides a framework for evaluating whether the health benefits of a treatment justify its additional costs. A key component of CEA is the estimation of treatment effects on both health outcomes and costs, which becomes challenging when using observational data, due to potential confounding. While advanced causal inference methods exist for use in such circumstances, their adoption in CEAs remains limited, with many studies relying on overly simplistic methods such as linear regression or propensity score matching. We believe that this is mainly due to health economists being generally unfamiliar with superior methodology. In this paper, we address this gap by introducing cost-effectiveness researchers to modern nonparametric regression models, with a particular focus on Bayesian Additive Regression Trees (BART). We provide practical guidance on how to implement BART in CEAs, including code examples, and discuss its advantages in producing more robust and credible estimates from observational data.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Dorie, V., Hill, J. L., Shalit, U., Scott, M., and Cervone, D (2019) Automated versus do-it-yourself methods for causal inference: lessons learned from a data analysis competition | 0.941 | 6 | 3 | 83% |
| 2 | Löthgren, M. and Zethraeus, N (2000) Definition, interpretation and calculation of cost-effectiveness acceptability curves | 0.843 | 3 | 3 | 100% |
| 3 | Esser, J., Maia, M., Parnell, A. C., Bosmans, J., van Dongen, H., Kl… (2024) Seemingly unrelated Bayesian additive regression trees for cost-effectiveness analyses in healthcare self | 0.811 | 4 | 2 | 100% |
| 4 | Baio, G (2018) Statistical modeling for health economic evaluations | 0.737 | 3 | 2 | 100% |
| 5 | Ben, Â. J., van Dongen, J. M., El Alili, M., Esser, J. L., Broulḱová… (2023) Conducting trial-based economic evaluations using R: a tutorial | 0.644 | 2 | 2 | 100% |
| 6 | Hernán, M. A. and Robins, J. M (2024) Causal Inference: What If\/ | 0.644 | 2 | 2 | 100% |
| 7 | Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L., and… (2015) Methods for the Economic Evaluation of Health Care Programmes\/ | 0.511 | 2 | 1 | 100% |
| 8 | Aronow, P. M. and Miller, B. T (2019) Foundations of agnostic statistics\/ | 0.405 | 1 | 1 | 100% |
| 9 | Aronow, P., Robins, J. M., Saarinen, T., Sävje, F., and Sekhon, J (2025) Nonparametric identification is not enough, but randomized controlled trials are | 0.405 | 1 | 1 | 100% |
| 10 | Briggs, A., Nixon, R., Dixon, S., and Thompson, S (2005) Parametric modelling of cost data: some simulation evidence | 0.405 | 1 | 1 | 100% |
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