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arxiv: 2606.31954 · v1 · pith:SVF7KF7Mnew · submitted 2026-06-30 · 📊 stat.ME

A Conformal Selection Framework for Individual Treatment Beneficiaries with Auxiliary External Data

Jiajun Liu , Ke Zhu , Xiaofei Wang This is my paper

Pith reviewed 2026-07-01 03:57 UTC · model grok-4.3

classification 📊 stat.ME
keywords conformal inferenceFDR controlheterogeneous treatment effectsprecision medicinerandomized controlled trialsreal-world datamultiple testing
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The pith

Conformal p-values calibrated on RCT data allow FDR-controlled selection of treatment beneficiaries using models trained on external data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a framework to select individual patients likely to benefit from a treatment while controlling the false discovery rate. It reformulates the problem of identifying beneficiaries based on conditional average treatment effects as a multiple-testing task. Conformal p-values are constructed using calibration from randomized controlled trial data, and the Benjamini-Hochberg procedure adjusts them for FDR control. External real-world data can train the underlying models to increase power when RCT samples are limited. Simulations verify FDR control, and a lung cancer case study demonstrates practical application in identifying profiles that benefit from limited resection.

Core claim

The framework reformulates CATE-based treatment-benefit selection as a multiple-testing problem. For each candidate, it tests whether the conditional treatment benefit exceeds a clinically meaningful threshold and constructs a conformal p-value using RCT-based calibration. These p-values are adjusted by the Benjamini-Hochberg procedure to control the false discovery rate among selected beneficiaries. External data can be used to train the treatment effect models while conformal calibration remains anchored in the RCT data.

What carries the argument

The conformal inference framework that constructs RCT-calibrated conformal p-values for testing if conditional treatment benefit exceeds a threshold, enabling FDR control via Benjamini-Hochberg adjustment.

Load-bearing premise

Conformal p-values constructed from RCT calibration remain valid for FDR control even when the treatment-effect model is trained on external data whose distribution may differ from the RCT.

What would settle it

A simulation study where the external data distribution differs substantially from the RCT and the realized false discovery rate among selected beneficiaries exceeds the target level.

Figures

Figures reproduced from arXiv: 2606.31954 by Jiajun Liu, Ke Zhu, Xiaofei Wang.

Figure 1
Figure 1. Figure 1: Flowchart of the proposed framework for identifying treatment beneficiaries [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FDR across different scenarios given covariate shift [PITH_FULL_IMAGE:figures/full_fig_p025_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Power across different scenarios given covariate shift [PITH_FULL_IMAGE:figures/full_fig_p026_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FDR across different scenarios given outcome drift ( [PITH_FULL_IMAGE:figures/full_fig_p028_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Power across different scenarios given outcome drift ( [PITH_FULL_IMAGE:figures/full_fig_p029_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of conformal 𝑝-value surfaces under ID and ED when limited resection is treatment of interest and Super Learner is base learner We use Restricted Mean Survival Time (RMST) as the outcome of interest. RMST is defined as 𝑌 = min(𝑇, 𝑡∗ ), where 𝑇 denotes survival time and 𝑡 ∗ is a pre-specified truncation time. RMST represents the expected survival time restricted to 𝑡 ∗ and is commonly used as an … view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of conformal 𝑝-values regarding age and tumor sizes when using Super Learner as the base learner Here, we present the case study results using Super Learner as the base learner, with corre￾sponding BART results provided in Supplementary Material D [PITH_FULL_IMAGE:figures/full_fig_p034_7.png] view at source ↗
read the original abstract

Identifying patients who are likely to benefit from a treatment is central to precision medicine and can guide follow-up trials, enrichment designs, and individualized decisions. Although randomized controlled trials (RCTs) provide evidence on efficacy, they are usually powered to estimate average treatment effects rather than patient-level benefit. Meanwhile, artificial intelligence and machine learning methods offer flexible tools for estimating heterogeneous treatment effects, especially when augmented by real-world data (RWD). However, in practice, these estimated effects are often translated into decisions through simple ranking or thresholding rules, which can ignore uncertainty and multiplicity when many patients are evaluated simultaneously. Motivated by this, we propose a model-agnostic conformal inference framework for uncertainty-aware beneficiary selection. The framework reformulates CATE-based treatment-benefit selection as a multiple-testing problem. For each candidate, we test whether the conditional treatment benefit exceeds a clinically meaningful threshold and construct a conformal p-value using RCT-based calibration. These p-values are then adjusted by the Benjamini-Hochberg procedure to control the false discovery rate (FDR) among selected beneficiaries. To improve efficiency when RCT sample sizes are limited, external data, such as RWD, can be used to train flexible treatment effect models, while conformal calibration remains anchored in the RCT data. It can be paired with conventional machine learning algorithms and emerging tabular foundation models. Simulations show that the framework maintains FDR control, with power depending on the base learner and external-data comparability. A case study in early-stage non-small-cell lung cancer illustrates how the method identifies candidate profiles with evidence of benefit from limited resection to reduce overtreatment.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The paper proposes a model-agnostic conformal inference framework for selecting individual treatment beneficiaries. It reformulates CATE-based selection as a multiple-testing problem, constructs conformal p-values calibrated exclusively on RCT data, and applies the Benjamini-Hochberg procedure to control FDR among selected beneficiaries. External data may be used to train the underlying treatment-effect model, while calibration and p-value construction remain anchored in the RCT; simulations are reported to confirm FDR control, and the method is illustrated in an early-stage non-small-cell lung cancer case study identifying profiles that may benefit from limited resection.

Significance. If the FDR guarantee holds, the framework supplies a statistically principled route to uncertainty-aware beneficiary selection that respects the limited size of RCTs while allowing flexible model training on external data. The explicit separation of training and calibration data, together with the reduction to standard conformal validity under RCT exchangeability, is a clean contribution that could be paired with existing CATE estimators or tabular foundation models.

minor comments (3)
  1. [Abstract] The abstract states that simulations confirm FDR control but supplies no quantitative results, error bars, or description of the simulation design; adding a brief summary table or figure reference would strengthen the claim.
  2. The construction of the conformal score and the precise definition of the conformal p-value (including any dependence on the estimated CATE) should be stated explicitly in the main text with an equation or algorithm box, even if the validity argument is standard.
  3. The lung-cancer case study would benefit from a table reporting the number of discoveries, estimated FDR, and a short description of the selected profiles to make the practical output concrete.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary of our work and the recommendation for minor revision. The report lists no specific major comments under the MAJOR COMMENTS section.

Circularity Check

0 steps flagged

No significant circularity; derivation self-contained

full rationale

The framework separates model training on external data from conformal p-value calibration and FDR control on RCT data. Standard conformal validity (exchangeability of calibration and test points) holds independently of how the score function is obtained, so the FDR guarantee does not reduce to a fitted quantity defined by the same data. No self-definitional steps, fitted inputs renamed as predictions, or load-bearing self-citations appear in the provided description or abstract. The central claim rests on established conformal properties rather than internal redefinition.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no explicit free parameters, axioms, or invented entities; the framework implicitly relies on standard conformal-inference exchangeability assumptions and RCT external-data comparability that are not stated or justified here.

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Reference graph

Works this paper leans on

114 extracted references · 41 canonical work pages

  1. [1]

    2025 , note =

    Precision Medicine , howpublished =. 2025 , note =

    2025

  2. [2]

    Journal of the Royal Statistical Society Series B: Statistical Methodology , volume=

    Conformal inference of counterfactuals and individual treatment effects , author=. Journal of the Royal Statistical Society Series B: Statistical Methodology , volume=. 2021 , publisher=

    2021

  3. [3]

    1990 , issn =

    Formal mode of statistical inference for causal effects , journal =. 1990 , issn =. doi:10.1016/0378-3758(90)90077-8 , url =

    work page doi:10.1016/0378-3758(90)90077-8 1990

  4. [4]

    Journal of Statistical Software , author=

    Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package , volume=. Journal of Statistical Software , author=. 2021 , pages=. doi:10.18637/jss.v097.i01 , number=

    work page doi:10.18637/jss.v097.i01 2021

  5. [5]

    , title =

    Rubin, Donald B. , title =. Journal of Educational Psychology , year =

  6. [6]

    Statistical Science , volume=

    Causal inference methods for combining randomized trials and observational studies: A review , author=. Statistical Science , volume=. 2024 , publisher=

    2024

  7. [7]

    2018 , doi =

    Kent, David M and Steyerberg, Ewout and van Klaveren, David , title =. 2018 , doi =. https://www.bmj.com/content/363/bmj.k4245.full.pdf , journal =

    2018

  8. [8]

    Social Science & Medicine , year =

    Deaton, Angus and Cartwright, Nancy , title =. Social Science & Medicine , year =. doi:10.1016/j.socscimed.2017.12.005 , pmid =

    work page doi:10.1016/j.socscimed.2017.12.005 2017

  9. [9]

    2018 , issn =

    Investigating heterogeneity of effects and associations using interaction terms , journal =. 2018 , issn =. doi:https://doi.org/10.1016/j.jclinepi.2017.09.012 , url =

    work page doi:10.1016/j.jclinepi.2017.09.012 2018

  10. [10]

    and Hayward, Rodney A

    Dahabreh, Issa J. and Hayward, Rodney A. and Kent, David M. , title =. International Journal of Epidemiology , year =. doi:10.1093/ije/dyw125 , pmid =

    work page doi:10.1093/ije/dyw125

  11. [11]

    2025 , eprint=

    Robust Estimation and Inference in Hybrid Controlled Trials for Binary Outcomes: A Case Study on Non-Small Cell Lung Cancer , author=. 2025 , eprint=

    2025

  12. [12]

    Bernoulli , number =

    Shu Yang and Siyi Liu and Donglin Zeng and Xiaofei Wang , title =. Bernoulli , number =. 2025 , doi =

    2025

  13. [13]

    Biometrics , year =

    Mao, Guangcai and Yang, Shu and Wang, Xiaofei , title =. Biometrics , year =. doi:10.1093/biomtc/ujaf131 , pmid =

    work page doi:10.1093/biomtc/ujaf131

  14. [14]

    2020 , eprint=

    Conformal prediction intervals for the individual treatment effect , author=. 2020 , eprint=

    2020

  15. [15]

    Candes , title =

    Ying Jin and Emmanuel J. Candes , title =. Journal of Machine Learning Research , year =

  16. [16]

    and Chang, Chung-Chou H

    Angus, Derek C. and Chang, Chung-Chou H. , title =. JAMA , year =. doi:10.1001/jama.2021.20552 , url =

    work page doi:10.1001/jama.2021.20552 2021

  17. [17]

    and Paulus, Jessica K

    Kent, David M. and Paulus, Jessica K. and van Klaveren, David and D'Agostino, Ralph and Goodman, Steve and Hayward, Rodney A. and Ioannidis, John P. A. and Patrick-Lake, Bray and Morton, Sally and Pencina, Michael and Raman, Gowri and Ross, Joseph S. and Selker, Harry P. and Varadhan, Ravi and Vickers, Andrew and Wong, John B. and Steyerberg, Ewout W. , t...

    work page doi:10.7326/m18-3667

  18. [18]

    Proceedings of the National Academy of Sciences , volume=

    Sensitivity analysis of individual treatment effects: A robust conformal inference approach , author=. Proceedings of the National Academy of Sciences , volume=. 2023 , publisher=

    2023

  19. [19]

    The Lancet Diabetes & Endocrinology , year =

    Baum, Aaron and Scarpa, Joseph and Bruzelius, Emilie and Tamler, Ronald and Basu, Sanjay and Faghmous, James , title =. The Lancet Diabetes & Endocrinology , year =. doi:10.1016/S2213-8587(17)30176-6 , pmid =

    work page doi:10.1016/s2213-8587(17)30176-6

  20. [20]

    BMJ , year =

    Inoue, Kosuke and Athey, Susan and Baicker, Katherine and Tsugawa, Yusuke , title =. BMJ , year =. doi:10.1136/bmj-2024-079377 , url =

    work page doi:10.1136/bmj-2024-079377 2024

  21. [21]

    , title =

    Inoue, Kosuke and Adomi, Motohiko and Efthimiou, Orestis and Komura, Toshiaki and Tsutsumi, Yusuke and Fujii, Tomoko and Kondo, Naoki and Onishi, Akira and Furukawa, Toshi A. , title =. Journal of Clinical Epidemiology , volume =. 2024 , doi =

    2024

  22. [22]

    The Annals of Applied Statistics , number =

    Kosuke Imai and Marc Ratkovic , title =. The Annals of Applied Statistics , number =. 2013 , doi =

    2013

  23. [23]

    Statistics in Medicine , author =

    Lasso estimation of hierarchical interactions for analyzing heterogeneity of treatment effect , volume =. Statistics in Medicine , author =. 2021 , pages =. doi:10.1002/sim.9132 , language =

    work page doi:10.1002/sim.9132 2021

  24. [24]

    Statistical Methods in Medical Research , author =

    Individual treatment effect prediction for amyotrophic lateral sclerosis patients , volume =. Statistical Methods in Medical Research , author =. 2018 , pages =. doi:10.1177/0962280217693034 , number =

    work page doi:10.1177/0962280217693034 2018

  25. [25]

    , author=

    A tutorial on conformal prediction. , author=. Journal of Machine Learning Research , volume=. 2008 , issn =

    2008

  26. [26]

    Statistics in Medicine , author =

    Risk controlled decision trees and random forests for precision. Statistics in Medicine , author =. 2022 , pages =. doi:10.1002/sim.9253 , number =

    work page doi:10.1002/sim.9253 2022

  27. [27]

    Journal of Clinical Oncology , volume =

    Uno, Hajime and Claggett, Brian and Tian, Lu and Inoue, Eisuke and Gallo, Paul and Miyata, Toshio and Schrag, Deborah and Takeuchi, Masahiro and Uyama, Yoshiaki and Zhao, Lihui and Skali, Hicham and Solomon, Scott and Jacobus, Susanna and Hughes, Michael and Packer, Milton and Wei, Lee-Jen , title =. Journal of Clinical Oncology , volume =. 2014 , doi =

    2014

  28. [28]

    Statistical Methods in Medical Research , volume =

    Per Kragh Andersen and Maja Pohar Perme , title =. Statistical Methods in Medical Research , volume =. 2010 , doi =

    2010

  29. [29]

    Journal of Statistical Software , author=

    Event History Regression with Pseudo-Observations: Computational Approaches and an Implementation in R , volume=. Journal of Statistical Software , author=. 2022 , pages=. doi:10.18637/jss.v102.i09 , number=

    work page doi:10.18637/jss.v102.i09 2022

  30. [30]

    and Conti, Massimo and Ashrafi, Ahmad S

    Altorki, Nasser and Wang, Xiaofei and Damman, Bryce and Mentlick, Jennifer and Landreneau, Rodney and Wigle, Dennis and Jones, David R. and Conti, Massimo and Ashrafi, Ahmad S. and Liberman, Moishe and de Perrot, Marc and Mitchell, John D. and Keenan, Robert and Bauer, Thomas and Miller, Daniel and Stinchcombe, Thomas E. , title =. The Journal of Thoracic...

    work page doi:10.1016/j.jtcvs.2023.07.008 2023

  31. [31]

    The Lancet , year =

    Segmentectomy versus lobectomy in small-sized peripheral non-small-cell lung cancer (JCOG0802/WJOG4607L): a multicentre, open-label, phase 3, randomised, controlled, non-inferiority trial , author =. The Lancet , year =. doi:10.1016/S0140-6736(21)02333-3 , url =

    work page doi:10.1016/s0140-6736(21)02333-3

  32. [32]

    , title =

    Tukey, John W. , title =. 1953 , note =

    1953

  33. [33]

    Powering

    H. Powering. Statistics in Medicine , year =. doi:10.1002/sim.70524 , month =

    work page doi:10.1002/sim.70524

  34. [34]

    , title =

    Benjamini, Yoav and Braun, Henry I. , title =. The Annals of Statistics , year =. doi:10.1214/aos/1043351247 , publisher =

    work page doi:10.1214/aos/1043351247

  35. [35]

    Ranganathan, Priya and Pramesh, C. S. and Buyse, Marc , title =. Perspectives in Clinical Research , year =. doi:10.4103/2229-3485.179436 , pmid =

    work page doi:10.4103/2229-3485.179436

  36. [36]

    2026 , eprint=

    Theoretical Foundations of Conformal Prediction , author=. 2026 , eprint=

    2026

  37. [37]

    Statistics in Medicine , year =

    Zhou, Tianjian and Ji, Yuan , title =. Statistics in Medicine , year =. doi:10.1002/sim.9191 , publisher =

    work page doi:10.1002/sim.9191

  38. [38]

    Biometrika , volume=

    Improving randomized controlled trial analysis via data-adaptive borrowing , author=. Biometrika , volume=. 2025 , publisher=

    2025

  39. [39]

    Proceedings of the 42nd International Conference on Machine Learning , pages =

    Enhancing Statistical Validity and Power in Hybrid Controlled Trials: A Randomization Inference Approach with Conformal Selective Borrowing , author =. Proceedings of the 42nd International Conference on Machine Learning , pages =. 2025 , editor =

    2025

  40. [40]

    The Econometrics Journal , volume =

    Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Duflo, Esther and Hansen, Christian and Newey, Whitney and Robins, James , title =. The Econometrics Journal , volume =. 2018 , month =. doi:10.1111/ectj.12097 , url =

    work page doi:10.1111/ectj.12097 2018

  41. [41]

    Journal of the American Statistical Association , volume=

    Distribution-free predictive inference for regression , author=. Journal of the American Statistical Association , volume=. 2018 , publisher=

    2018

  42. [42]

    Biometrical Journal , author =

    Identification of subpopulations with distinct treatment benefit rate using the. Biometrical Journal , author =. 2016 , keywords =. doi:10.1002/bimj.201500180 , number =

    work page doi:10.1002/bimj.201500180 2016

  43. [43]

    Bayesian Analysis , author =

    Bayesian. Bayesian Analysis , author =. 2020 , pages =. doi:10.1214/19-BA1195 , number =

    work page doi:10.1214/19-ba1195 2020

  44. [44]

    American Journal of Epidemiology , author =

    Performance. American Journal of Epidemiology , author =. 2021 , pmid =. doi:10.1093/aje/kwab220 , number =

    work page doi:10.1093/aje/kwab220 2021

  45. [45]

    The International Journal of Biostatistics , author =

    The optimal dynamic treatment rule superlearner: considerations, performance, and application to criminal justice interventions , volume =. The International Journal of Biostatistics , author =. 2021 , pmid =. doi:10.1515/ijb-2020-0127 , number =

    work page doi:10.1515/ijb-2020-0127 2021

  46. [46]

    Annual Review of Statistics and Its Application , author =

    Bayesian. Annual Review of Statistics and Its Application , author =. 2020 , note =. doi:10.1146/annurev-statistics-031219-041110 , language =

    work page doi:10.1146/annurev-statistics-031219-041110 2020

  47. [47]

    Proceedings of the National Academy of Sciences of the United States of America , author =

    Recursive partitioning for heterogeneous causal effects , volume =. Proceedings of the National Academy of Sciences of the United States of America , author =. 2016 , pmid =. doi:10.1073/pnas.1510489113 , number =

    work page doi:10.1073/pnas.1510489113 2016

  48. [48]

    Kennedy , title =

    Edward H. Kennedy , title =. Electronic Journal of Statistics , number =. 2023 , doi =

    2023

  49. [49]

    Biometrika , volume =

    Nie, Xinkun and Wager, Stefan , title =. Biometrika , volume =. 2021 , month =. doi:10.1093/biomet/asaa076 , url =

    work page doi:10.1093/biomet/asaa076 2021

  50. [50]

    https://doi.org/10.1073/pnas.1804597116 Publisher: Proceedings of the National Academy of Sciences

    Metalearners for estimating heterogeneous treatment effects using machine learning , volume =. Proceedings of the National Academy of Sciences , author =. 2019 , pages =. doi:10.1073/pnas.1804597116 , number =

    work page doi:10.1073/pnas.1804597116 2019

  51. [51]

    Statistics in Medicine , volume =

    Lipkovich, Ilya and Svensson, David and Ratitch, Bohdana and Dmitrienko, Alex , title =. Statistics in Medicine , volume =. doi:https://doi.org/10.1002/sim.10167 , url =. https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.10167 , year =

    work page doi:10.1002/sim.10167

  52. [52]

    Journal of the Royal Statistical Society

    Controlling the false discovery rate: a practical and powerful approach to multiple testing , author=. Journal of the Royal Statistical Society. Series B (Methodological) , volume=. 1995 , publisher=

    1995

  53. [53]

    The Annals of Statistics , volume=

    The control of the false discovery rate in multiple testing under dependency , author=. The Annals of Statistics , volume=. 2001 , publisher=. doi:10.1214/aos/1013699998 , url=

    work page doi:10.1214/aos/1013699998 2001

  54. [54]

    The Annals of Statistics , volume=

    Testing for outliers with conformal p-values , author=. The Annals of Statistics , volume=. 2023 , publisher=. doi:10.1214/22-AOS2240 , url=

    work page doi:10.1214/22-aos2240 2023

  55. [55]

    Journal of the Royal Statistical Society Series B , author=

    -investing: a procedure for sequential control of expected false discoveries , year=. Journal of the Royal Statistical Society Series B , author=. doi:10.1111/j.1467-9868.2007.00643.x , url=

    work page doi:10.1111/j.1467-9868.2007.00643.x 2007

  56. [56]

    Journal of the Royal Statistical Society: Series B (Statistical Methodology) , year =

    Aharoni, Ehud and Rosset, Saharon , title =. Journal of the Royal Statistical Society: Series B (Statistical Methodology) , year =. doi:10.1111/rssb.12048 , url =

    work page doi:10.1111/rssb.12048

  57. [57]

    The Annals of Statistics , volume =

    Adel Javanmard and Andrea Montanari , title =. The Annals of Statistics , volume =. 2018 , month = apr, doi =

    2018

  58. [58]

    2015 , eprint=

    On Online Control of False Discovery Rate , author=. 2015 , eprint=

    2015

  59. [59]

    Sequential Analysis , volume=

    Sequential Tests of Multiple Hypotheses Controlling False Discovery and Nondiscovery Rates , author=. Sequential Analysis , volume=. 2020 , publisher=

    2020

  60. [60]

    2017 , eprint=

    Sequential Multiple Testing , author=. 2017 , eprint=

    2017

  61. [61]

    2024 , eprint=

    False Discovery Control in Multiple Testing: A Brief Overview of Theories and Methodologies , author=. 2024 , eprint=

    2024

  62. [62]

    Statistical Methods in Medical Research , volume=

    Online control of the False Discovery Rate in group-sequential platform trials , author=. Statistical Methods in Medical Research , volume=. 2022 , publisher=

    2022

  63. [63]

    Online control of the false discovery rate with decaying memory , url =

    Ramdas, Aaditya and Yang, Fanny and Wainwright, Martin J and Jordan, Michael I , booktitle =. Online control of the false discovery rate with decaying memory , url =

  64. [64]

    Proceedings of the 35th International Conference on Machine Learning (ICML 2018) , volume =

    SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate , author =. Proceedings of the 35th International Conference on Machine Learning (ICML 2018) , volume =. 2018 , publisher =

    2018

  65. [65]

    2025 , eprint=

    ACS: An interactive framework for conformal selection , author=. 2025 , eprint=

    2025

  66. [66]

    Lara Maleyeff and Shirin Golchi and Erica E. M. Moodie and Marie Hudson , title =. Biometrics , volume =. 2024 , doi =

    2024

  67. [67]

    Journal of the Royal Statistical Society Series B: Statistical Methodology , volume =

    Liang, Ziyi and Sesia, Matteo and Sun, Wenguang , title =. Journal of the Royal Statistical Society Series B: Statistical Methodology , volume =. 2024 , month =. doi:10.1093/jrsssb/qkad138 , url =

    work page doi:10.1093/jrsssb/qkad138 2024

  68. [68]

    The Annals of Statistics , number =

    William Fithian and Lihua Lei , title =. The Annals of Statistics , number =. 2022 , doi =

    2022

  69. [69]

    2025 , eprint=

    Cross-World Assumption and Refining Prediction Intervals for Individual Treatment Effects , author=. 2025 , eprint=

    2025

  70. [70]

    2025 , eprint=

    Online Conformal Selection with Accept-to-Reject Changes , author=. 2025 , eprint=

    2025

  71. [71]

    and Ahmad, Zaid and van der Laan, Mark , booktitle =

    Alaa, Ahmed M. and Ahmad, Zaid and van der Laan, Mark , booktitle =. Conformal Meta-learners for Predictive Inference of Individual Treatment Effects , volume =

  72. [72]

    Proceedings of the First Conference on Causal Learning and Reasoning , pages =

    Integrative R -learner of heterogeneous treatment effects combining experimental and observational studies , author =. Proceedings of the First Conference on Causal Learning and Reasoning , pages =. 2022 , editor =

    2022

  73. [73]

    2022 , eprint=

    Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects , author=. 2022 , eprint=

    2022

  74. [74]

    2024 , eprint=

    Conformal Diffusion Models for Individual Treatment Effect Estimation and Inference , author=. 2024 , eprint=

    2024

  75. [75]

    Mathematical Modelling , year =

    A New Approach to Causal Inference in Mortality Studies with Sustained Exposure Periods---Application to Control of the Healthy Worker Survivor Effect , author =. Mathematical Modelling , year =

  76. [76]

    JAMA , year =

    Analysis and interpretation of treatment effects in subgroups of patients in randomized clinical trials , author =. JAMA , year =

  77. [77]

    Biometrics , year =

    Testing for qualitative interactions between treatment effects and patient subsets , author =. Biometrics , year =

  78. [78]

    2024 , isbn =

    Chen, Zonghao and Guo, Ruocheng and Ton, Jean-Francois and Liu, Yang , title =. 2024 , isbn =. doi:10.1145/3637528.3671976 , booktitle =

    work page doi:10.1145/3637528.3671976 2024

  79. [79]

    2025 , eprint=

    Conformal Convolution and Monte Carlo Meta-learners for Predictive Inference of Individual Treatment Effects , author=. 2025 , eprint=

    2025

  80. [80]

    2025 , eprint=

    On the Role of Surrogates in Conformal Inference of Individual Causal Effects , author=. 2025 , eprint=

    2025

Showing first 80 references.