Fernando Delbianco, Fernando Tohmé
arXiv 10 Aug 2026 · Statistics — Methodology
arXiv:2608.09612 · PDF · Extracted main text
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.
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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 | Lei, Lihua and Candès, Emmanuel J (2021) Conformal inference of counterfactuals and individual treatment effects | 1.000 | 6 | 4 | 100% |
| 2 | Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Du… (2018) Double/debiased machine learning for treatment and structural parameters | 0.928 | 4 | 3 | 100% |
| 3 | Wager, Stefan and Athey, Susan (2018) Estimation and Inference of Heterogeneous Treatment Effects using Random Forests | 0.928 | 4 | 3 | 100% |
| 4 | Vovk, Vladimir and Gammerman, Alexander and Shafer, Glenn (2005) Algorithmic learning in a random world | 0.843 | 3 | 3 | 100% |
| 5 | Hill, Jennifer L (2011) Bayesian Nonparametric Modeling for Causal Inference | 0.644 | 2 | 2 | 100% |
| 6 | Meng, Xiao-Li (2018) Statistical Paradises and Paradoxes in Big Data (I): Law of Large Populations, Big Data Paradox, and the 2016 US Presidential El… | 0.644 | 2 | 2 | 100% |
| 7 | Peters, Jonas and Bühlmann, Peter and Meinshausen, Nicolai (2016) Causal Inference by Using Invariant Prediction: Identification and Confidence Intervals | 0.644 | 2 | 2 | 100% |
| 8 | Peters, Jonas and Janzing, Dominik and Schölkopf, Bernhard (2017) Elements of Causal Inference: Foundations and Learning Algorithms | 0.644 | 2 | 2 | 100% |
| 9 | Ahrens, Achim and Aitken, Christopher and Schaffer, Mark E (2020) Using machine learning methods to support causal inference in econometrics | 0.405 | 1 | 1 | 100% |
| 10 | Cunningham, Scott (2026) Claude Code 50: Claude is Holding On To Its Reasons | 0.405 | 1 | 1 | 100% |
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