Local conformal prediction for individual causal effects
Abstract
Standard CATE estimators become inadequate under strong treatment-effect heterogeneity: confidence intervals for conditional means need not cover individual counterfactual effects. We propose an Individualized Causal Prediction (ICP) framework that constructs finite-sample valid conformal prediction intervals for the individual causal effect of a specific query unit. The method localizes calibration to a causally relevant neighborhood using cosine similarity weighted by Causal Forest variable importance, augments small local samples synthetically, and calibrates intervals with doubly robust AIPW conformity scores satisfying Neyman orthogonality. Under standard identifying assumptions (SUTVA and strong ignorability) and an outcome-independent calibration-set selection condition, the resulting intervals attain marginal coverage at the nominal level. The local design also supports approximately conditional coverage by making calibration scores more representative of the query unit. Experiments on a high-heterogeneity synthetic dataset and the IHDP benchmark demonstrate that local strategies improve point accuracy over global baselines while maintaining nominal or above-nominal coverage.
Related
- Metric Conformal Prediction via the Conditional Expected Local Radius
- Abstention Spends Its Budget on Local Label Variance More Than on Novelty: Auditing Conformal Calibration and Selective Prediction under Molecular Scaffold Shift
- Conformal Individual Treatment Effect Estimation under Networked Interference
- From Type to Token: Petri Nets, Individual Token Philosophy, and Individualized Causal Effects
- lepidoptera: A Domain-Agnostic Engine for Causal Simulation with Mixed Variable Types, Vine Copula Dependence, and Individual-Level Counterfactual Propagation
BibTeX
@misc{delbianco2026local,
title = {{Local conformal prediction for individual causal effects}},
author = {Fernando Delbianco and Fernando Tohme},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2608.09612},
eprint = {2608.09612},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.09612},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Local conformal prediction for individual causal effects
AU - Fernando Delbianco
AU - Fernando Tohme
PY - 2026
DA - 2026/10/20
PB - arXiv
DO - 10.48550/arxiv.2608.09612
UR - https://arxiv.org/abs/2608.09612
AB - Standard CATE estimators become inadequate under strong treatment-effect heterogeneity: confidence intervals for conditional means need not cover individual counterfactual effects. We propose an Individualized Causal Prediction (ICP) framework that constructs finite-sample valid conformal prediction intervals for the individual causal effect of a specific query unit. The method localizes calibration to a causally relevant neighborhood using cosine similarity weighted by Causal Forest variable importance, augments small local samples synthetically, and calibrates intervals with doubly robust AIPW conformity scores satisfying Neyman orthogonality. Under standard identifying assumptions (SUTVA and strong ignorability) and an outcome-independent calibration-set selection condition, the resulting intervals attain marginal coverage at the nominal level. The local design also supports approximately conditional coverage by making calibration scores more representative of the query unit. Experiments on a high-heterogeneity synthetic dataset and the IHDP benchmark demonstrate that local strategies improve point accuracy over global baselines while maintaining nominal or above-nominal coverage.
ER -