mlsanityData to 14 Oct 2026

Conformal Meta-learners for Predictive Inference of Individual Treatment Effects

arXiv 2308.14895 ↗First posted 28 Aug 2023cs.LGcited by 23

Not on the map. Why?

Abstract (arXiv)

We investigate the problem of machine learning-based (ML) predictive inference on individual treatment effects (ITEs). Previous work has focused primarily on developing ML-based meta-learners that can provide point estimates of the conditional average treatment effect (CATE); these are model-agnostic approaches for combining intermediate nuisance estimates to produce estimates of CATE. In this paper, we develop conformal meta-learners, a general framework for issuing predictive intervals for ITEs by applying the standard conformal prediction (CP) procedure on top of CATE meta-learners. We focus on a broad class of meta-learners based on two-stage pseudo-outcome regression and develop a stochastic ordering framework to study their validity. We show that inference with conformal meta-learners is marginally valid if their (pseudo outcome) conformity scores stochastically dominate oracle conformity scores evaluated on the unobserved ITEs. Additionally, we prove that commonly used CATE meta-learners, such as the doubly-robust learner, satisfy a model- and distribution-free stochastic (or convex) dominance condition, making their conformal inferences valid for practically-relevant levels of target coverage. Whereas existing procedures conduct inference on nuisance parameters (i.e., potential outcomes) via weighted CP, conformal meta-learners enable direct inference on the target parameter (ITE). Numerical experiments show that conformal meta-learners provide valid intervals with competitive efficiency while retaining the favorable point estimation properties of CATE meta-learners.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Builds on 21 of its 51 references that are in the map. First posted on arXiv 28 Aug 2023.

Builds on

21 of 51 references.

References · 21

  1. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,917
  2. 2019VisualBERT: A Simple and Performant Baseline for Vision and Languagecited by 1,296
  3. 2016Distribution-Free Predictive Inference For Regressioncited by 668
  4. 2019Conformalized Quantile Regressioncited by 629
  5. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 600
  6. 2019Conformal Prediction Under Covariate Shiftcited by 519
  7. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 478
  8. 2021Adaptive Conformal Inference Under Distribution Shiftcited by 375
  9. 2022Conformal prediction beyond exchangeabilitycited by 355
  10. 2016Learning Representations for Counterfactual Inferencecited by 329
  11. 2017Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processescited by 158
  12. 2020Conformal Inference of Counterfactuals and Individual Treatment Effectscited by 151
9 more
  1. 2017Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competitioncited by 106
  2. 2021Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalhocited by 77
  3. 2021On Inductive Biases for Heterogeneous Treatment Effect Estimationcited by 75
  4. 2021Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approachcited by 62
  5. 2013Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknifecited by 44
  6. 2021Conformal Sensitivity Analysis for Individual Treatment Effectscited by 40
  7. 2022Conformal Off-Policy Prediction in Contextual Banditscited by 25
  8. 2022Conformal Off-policy Predictioncited by 14
  9. 2019Assessing Treatment Effect Variation in Observational Studies: Results from a Data Challengecited by 12

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 8; 2025: 5; 2026: 9.

23 papers.

Sort:

By area:

  1. 2024Causal machine learning for predicting treatment outcomescited by 74
  2. 2024Conformal Prediction: A Data Perspectivecited by 25
  3. 2024Conformal Prediction for Causal Effects of Continuous Treatmentscited by 19
  4. 2024Conformal Counterfactual Inference under Hidden Confoundingcited by 10
  5. 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learnercited by 10
  6. 2025Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Datacited by 10
  7. 2024Conformal Convolution and Monte Carlo Meta-learners for Predictive Inference of Individual Treatment Effectscited by 8
  8. 2025Synthetic Counterfactual Labels for Efficient Conformal Counterfactual Inferencecited by 2
  9. 2024Conformal Diffusion Models for Individual Treatment Effect Estimation and Inferencecited by 1
  10. 2025Cross-World Assumption and Refining Prediction Intervals for Individual Treatment Effectscited by 1
  11. 2025A Systematic Review of Conformal Inference Procedures for Treatment Effect Estimation: Methods and Challengescited by 1
  12. 2026Retrospective Counterfactual Prediction by Conditioning on the Factual Outcome: A Cross-World Approachcited by 1
11 more
  1. 2023Bridging the gap: Towards an Expanded Toolkit for AI-driven Decision-Making in the Public Sectorcited by 0
  2. 2024From Text to Treatment Effects: A Meta-Learning Approach to Handling Text-Based Confoundingcited by 0
  3. 2025Prediction Intervals for Individual Treatment Effects in a Multiple Decision Point Framework using Conformal Inferencecited by 0
  4. 2026Partial Causal Structure Learning for Valid Selective Conformal Inference under Interventionscited by 0
  5. 2026Debiased Machine Learning for Conformal Prediction of Counterfactual Outcomes Under Runtime Confoundingcited by 0
  6. 2026Bayesian X-Learner: Calibrated Posterior Inference for Heterogeneous Treatment Effects under Heavy-Tailed Outcomescited by 0
  7. 2026Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networkscited by 0
  8. 2026Set-Valued Policy Learningcited by 0
  9. 2026CausalGuard: Conformal Inference under Graph Uncertaintycited by 0
  10. 2026Calibrated Inference for the Conditional Average Treatment Effect in the Few-Placebo Regime via Gaussian Processescited by 0
  11. 2026Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Predictioncited by 0