Conformal Prediction for Causal Effects of Continuous Treatments

2024-07-03

arXiv.org

AI Summary

This work addresses the open problem of quantifying uncertainty in causal effect estimation under continuous treatments, overcoming key limitations of existing conformal prediction methods—which are restricted to binary treatments and require known propensity scores. We propose the first model-agnostic, finite-sample valid conformal prediction framework for continuous interventions. Our approach explicitly accounts for the additional uncertainty induced by propensity score estimation, provides theoretically guaranteed prediction intervals, and includes an efficient algorithm for interval construction. Experiments on synthetic and real-world datasets demonstrate that our method strictly achieves the nominal statistical coverage level and significantly outperforms baseline approaches. To our knowledge, this is the first rigorously validated tool for constructing reliable confidence intervals for potential outcomes under continuous interventions—enabling trustworthy decision-making in safety-critical domains such as personalized medicine.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Calibration & Uncertainty Quantification
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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 prediction intervals for 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.

Problem

• Develops conformal prediction for continuous treatment causal effects
• Addresses uncertainty from unknown propensity score estimation
• Provides valid prediction intervals without restrictive assumptions

Innovation

• Conformal prediction for continuous treatments
• Valid intervals with estimated propensity scores
• Finite-sample guarantees for causal effects