mlsanityData to 7 Oct 2026

Multi-Source Conformal Inference Under Distribution Shift

arXiv 2405.09331 ↗First posted 15 May 2024stat.ME stat.MLcited by 11

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Abstract (arXiv)

Recent years have experienced increasing utilization of complex machine learning models across multiple sources of data to inform more generalizable decision-making. However, distribution shifts across data sources and privacy concerns related to sharing individual-level data, coupled with a lack of uncertainty quantification from machine learning predictions, make it challenging to achieve valid inferences in multi-source environments. In this paper, we consider the problem of obtaining distribution-free prediction intervals for a target population, leveraging multiple potentially biased data sources. We derive the efficient influence functions for the quantiles of unobserved outcomes in the target and source populations, and show that one can incorporate machine learning prediction algorithms in the estimation of nuisance functions while still achieving parametric rates of convergence to nominal coverage probabilities. Moreover, when conditional outcome invariance is violated, we propose a data-adaptive strategy to upweight informative data sources for efficiency gain and downweight non-informative data sources for bias reduction. We highlight the robustness and efficiency of our proposals for a variety of conformal scores and data-generating mechanisms via extensive synthetic experiments. Hospital length of stay prediction intervals for pediatric patients undergoing a high-risk cardiac surgical procedure between 2016-2022 in the U.S. illustrate the utility of our methodology.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 24 of its 41 references that are in the map. First posted on arXiv 15 May 2024.

Builds on

24 of 41 references.

References · 24

  1. 2016Distribution-Free Predictive Inference For Regressioncited by 657
  2. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  3. 2019Conformal Prediction Under Covariate Shiftcited by 511
  4. 2022Conformal prediction beyond exchangeabilitycited by 348
  5. 2020Conformal Inference of Counterfactuals and Individual Treatment Effectscited by 150
  6. 2021Distribution-free uncertainty quantification for classification under label shiftcited by 99
  7. 2021Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approachcited by 62
  8. 2023Federated Conformal Predictors for Distributed Uncertainty Quantificationcited by 46
  9. 2021Semiparametric counterfactual density estimationcited by 42
  10. 2021Conformal Sensitivity Analysis for Individual Treatment Effectscited by 40
  11. 2022Doubly Robust Calibration of Prediction Sets under Covariate Shiftcited by 40
  12. 2023Diagnosing Model Performance Under Distribution Shiftcited by 37
12 more
  1. 2018Distribution-Free Prediction Sets for Two-Layer Hierarchical Modelscited by 34
  2. 2023Conformal Prediction for Federated Uncertainty Quantification Under Label Shiftcited by 29
  3. 2023One-Shot Federated Conformal Predictioncited by 26
  4. 2021Federated Causal Inference in Heterogeneous Observational Datacited by 24
  5. 2021Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approachcited by 20
  6. 2023An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effectscited by 19
  7. 2021Federated Adaptive Causal Estimation (FACE) of Target Treatment Effectscited by 16
  8. 2023Distribution-free inference with hierarchical datacited by 14
  9. 2023Multiply Robust Federated Estimation of Targeted Average Treatment Effectscited by 13
  10. 2023Robust Inference for Federated Meta-Learningcited by 7
  11. 2023Diagnosing the role of observable distribution shift in scientific replicationscited by 7
  12. 2022Privacy-Preserving, Communication-Efficient, and Target-Flexible Hospital Quality Measurementcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 6; 2026: 3.

11 papers.

  1. 2024Conformal Prediction: A Data Perspectivecited by 25
  2. 2025Wasserstein-regularized Conformal Prediction under General Distribution Shiftcited by 14
  3. 2024What If We Had Used a Different App? Reliable Counterfactual KPI Analysis in Wireless Systemscited by 5
  4. 2025Conformal Prediction Under Generalized Covariate Shift with Posterior Driftcited by 3
  5. 2025Conformal Prediction in The Loop: A Feedback-Based Uncertainty Model for Trajectory Optimizationcited by 3
  6. 2025Federated Conditional Conformal Prediction via Generative Modelscited by 2
  7. 2026Multi-Distribution Robust Conformal Predictioncited by 1
  8. 2025Rate doubly robust estimation for weighted average treatment effectscited by 0
  9. 2025IDOL: Meeting Diverse Distribution Shifts with Prior Physics for Tropical Cyclone Multi-Task Estimationcited by 0
  10. 2026Debiased Machine Learning for Conformal Prediction of Counterfactual Outcomes Under Runtime Confoundingcited by 0
  11. 2026Multi-source conformal prediction: leveraging heterogeneity via localizationcited by 0