mlsanityData to 14 Oct 2026

Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding

arXiv 2306.09520 ↗First posted 15 Jun 2023cs.LG cs.AI stat.MEcited by 7

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Abstract (arXiv)

Causal inference of exact individual treatment outcomes in the presence of hidden confounders is rarely possible. Recent work has extended prediction intervals with finite-sample guarantees to partially identifiable causal outcomes, by means of a sensitivity model for hidden confounding. In deep learning, predictors can exploit their inductive biases for better generalization out of sample. We argue that the structure inherent to a deep ensemble should inform a tighter partial identification of the causal outcomes that they predict. We therefore introduce an approach termed Caus-Modens, for characterizing causal outcome intervals by modulated ensembles. We present a simple approach to partial identification using existing causal sensitivity models and show empirically that Caus-Modens gives tighter outcome intervals, as measured by the necessary interval size to achieve sufficient coverage. The last of our three diverse benchmarks is a novel usage of GPT-4 for observational experiments with unknown but probeable ground truth.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 40 of its 64 references that are in the map. First posted on arXiv 15 Jun 2023.

Builds on

40 of 64 references.

References · 40

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
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28 more
  1. 2022Diverse Weight Averaging for Out-of-Distribution Generalizationcited by 148
  2. 2023Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Modelscited by 148
  3. 2021Inductive Biases and Variable Creation in Self-Attention Mechanismscited by 144
  4. 2020Uncertainty Quantification and Deep Ensemblescited by 139
  5. 2022Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?cited by 125
  6. 2018High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approachcited by 117
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  11. 2020Identifying Causal-Effect Inference Failure with Uncertainty-Aware Modelscited by 57
  12. 2018Interval Estimation of Individual-Level Causal Effects Under Unobserved Confoundingcited by 51
  13. 2021Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confoundingcited by 50
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  15. 2021Causal Effect Inference for Structured Treatmentscited by 39
  16. 2021Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancingcited by 32
  17. 2023A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methodscited by 32
  18. 2021Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Datacited by 28
  19. 2021Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confoundingcited by 28
  20. 2022Unifying Data Perspectivism and Personalization: An Application to Social Normscited by 22
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  22. 2023When are ensembles really effective?cited by 21
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  25. 2020Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applicationscited by 16
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 1; 2026: 2.

7 papers.

  1. 2023A Neural Framework for Generalized Causal Sensitivity Analysiscited by 17
  2. 2023Causal Fairness under Unobserved Confounding: A Neural Sensitivity Frameworkcited by 10
  3. 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learnercited by 10
  4. 2024Estimating Causal Effects of Text Interventions Leveraging LLMscited by 4
  5. 2025Conformal Prediction with Corrupted Labels: Uncertain Imputation and Robust Re-weightingcited by 3
  6. 2026Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networkscited by 3
  7. 2026Uncertainty-aware Causal Decision Making via Effect Bound Decompositioncited by 0