A systematic review of conformal inference procedures for treatment effect estimation: methods and challenges
Treatment effect estimation is essential for informed decision-making in many fields such as healthcare, economics, and public policy. Whereas flexible machine learning models have seen extensive use in estimating heterogeneous treatment effects, assessing the uncertainty associated with their point...
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| Autori principali: | , , |
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| Natura: | Article (Journal) |
| Lingua: | inglese |
| Pubblicazione: |
13 April 2026
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| In: |
IEEE access
Year: 2026, Volume: 14, Pages: 62965-62980 |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/ACCESS.2026.3683260 |
| Accesso online: | Verlag, kostenfrei, Volltext: https://doi.org/10.1109/ACCESS.2026.3683260 Verlag, kostenfrei, Volltext: https://ieeexplore.ieee.org/document/11480086/authors 
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| Note sull'autore: | Pascal Memmesheimer, Vincent Heuveline, and Jürgen Hesser, (Member, IEEE) |
| Riassunto: | Treatment effect estimation is essential for informed decision-making in many fields such as healthcare, economics, and public policy. Whereas flexible machine learning models have seen extensive use in estimating heterogeneous treatment effects, assessing the uncertainty associated with their point estimates remains a significant challenge, especially in observational settings where treatment assignment is confounded by covariates. Recent advancements in conformal prediction address this limitation by allowing for inexpensive computation, as well as distribution shifts, while still providing frequentist, finite-sample coverage guarantees under minimal assumptions for any point-predictor model. These advancements hold significant potential for improving decision-making in high-stakes environments. In this work, we perform a systematic review regarding conformal prediction methods for treatment effect estimation and provide for both areas the necessary theoretical background. Through a systematic filtering process, we select and analyze eleven key papers, identifying and describing current state-of-the-art methods. Our findings suggest future research directions and reveal key challenges to be addressed. |
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| Descrizione del documento: | Veröffentlicht: 13. April 2026, Artikelversion: 28. April 2026 Gesehen am 19.06.2026 |
| Descrizione fisica: | Online Resource |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/ACCESS.2026.3683260 |