Conformal Prediction for Causal Effects of Continuous Treatments
MCML Authors
Maresa Schröder
Jonas Schweisthal
Valentyn Melnychuk
Stefan Feuerriegel
Prof. Dr.
Principal Investigator
Abstract
Maresa Schröder
Jonas Schweisthal
Valentyn Melnychuk
Stefan Feuerriegel
Prof. Dr.
Principal Investigator
Abstract
Uncertainty quantification of causal effects is crucial for safety-critical applications such as personalized medicine. A powerful approach for this is conformal prediction, which has several practical benefits due to model-agnostic finite-sample guarantees. Yet, existing methods for conformal prediction of causal effects are limited to binary/discrete treatments and make highly restrictive assumptions, such as known propensity scores. In this work, we provide a novel conformal prediction method for potential outcomes of continuous treatments. We account for the additional uncertainty introduced through propensity estimation so that our conformal prediction intervals are valid even if the propensity score is unknown. Our contributions are three-fold: (1) We derive finite-sample validity guarantees for prediction intervals of potential outcomes of continuous treatments. (2) We provide an algorithm for calculating the derived intervals. (3) We demonstrate the effectiveness of the conformal prediction intervals in experiments on synthetic and real-world datasets. To the best of our knowledge, we are the first to propose conformal prediction for continuous treatments when the propensity score is unknown and must be estimated from data.
inproceedings SFS+25a
NeurIPS 2025
39th Conference on Neural Information Processing Systems. San Diego, CA, USA, Nov 30-Dec 07, 2025. To be published. Preprint available.
Authors
M. Schröder • D. Frauen • J. Schweisthal • K. Heß • V. Melnychuk • S. FeuerriegelLinks
URLResearch Area
BibTeXKey: SFS+25a