mlsanityData to 7 Oct 2026

Counterfactual Phenotyping with Censored Time-to-Events

arXiv 2202.11089 ↗First posted 22 Feb 2022cs.LG stat.AP stat.MEcited by 13

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Abstract (arXiv)

Estimation of treatment efficacy of real-world clinical interventions involves working with continuous outcomes such as time-to-death, re-hospitalization, or a composite event that may be subject to censoring. Counterfactual reasoning in such scenarios requires decoupling the effects of confounding physiological characteristics that affect baseline survival rates from the effects of the interventions being assessed. In this paper, we present a latent variable approach to model heterogeneous treatment effects by proposing that an individual can belong to one of latent clusters with distinct response characteristics. We show that this latent structure can mediate the base survival rates and helps determine the effects of an intervention. We demonstrate the ability of our approach to discover actionable phenotypes of individuals based on their treatment response on multiple large randomized clinical trials originally conducted to assess appropriate treatments to reduce cardiovascular risk.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 13 of its 48 references that are in the map. First posted on arXiv 22 Feb 2022.

Builds on

13 of 48 references.

References · 13

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Networkcited by 354
  3. 2020Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Riskscited by 70
  4. 2016Deep Survival Analysiscited by 60
  5. 2021Deep Cox Mixtures for Survival Regressioncited by 43
  6. 2018Adversarial Time-to-Event Modelingcited by 33
  7. 2021X-CAL: Explicit Calibration for Survival Analysiscited by 30
  8. 2021A Deep Variational Approach to Clustering Survival Datacited by 25
  9. 2021SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Datacited by 25
  10. 2020Survival Cluster Analysiscited by 17
  11. 2017Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effectcited by 15
  12. 2016Gaussian Processes for Survival Analysiscited by 14
1 more
  1. 2019Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelinescited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 5; 2024: 2; 2025: 2; 2026: 2.

13 papers.

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  1. 2023Deep Learning for Survival Analysis: A Reviewcited by 75
  2. 2023Neural Fine-Gray: Monotonic neural networks for competing riskscited by 20
  3. 2022auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Datacited by 17
  4. 2023Modeling Events and Interactions through Temporal Processes -- A Surveycited by 5
  5. 2025Competing Risks: Impact on Risk Estimation and Algorithmic Fairnesscited by 2
  6. 2022BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effectcited by 1
  7. 2023Recovering Sparse and Interpretable Subgroups with Heterogeneous Treatment Effects with Censored Time-to-Event Outcomescited by 1
  8. 2023Maximum Likelihood Estimation of Flexible Survival Densities with Importance Samplingcited by 1
  9. 2024Variational Deep Survival Machines: Survival Regression with Censored Outcomescited by 0
  10. 2024Identifying treatment response subgroups in observational time-to-event datacited by 0
  11. 2025ADHAM: Additive Deep Hazard Analysis Mixtures for Interpretable Survival Regressioncited by 0
  12. 2026Counterfactual Understanding via Retrieval-aware Multimodal Modeling for Time-to-Event Survival Predictioncited by 0
1 more
  1. 2026SurvCF(t): Counterfactual Explanations for Survival Analysis in Predictive Maintenance Multivariate Time Series Datacited by 0