mlsanityData to 14 Oct 2026

A Neural Framework for Generalized Causal Sensitivity Analysis

arXiv 2311.16026 ↗First posted 27 Nov 2023cs.LG stat.MLcited by 17

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Abstract (arXiv)

Unobserved confounding is common in many applications, making causal inference from observational data challenging. As a remedy, causal sensitivity analysis is an important tool to draw causal conclusions under unobserved confounding with mathematical guarantees. In this paper, we propose NeuralCSA, a neural framework for generalized causal sensitivity analysis. Unlike previous work, our framework is compatible with (i) a large class of sensitivity models, including the marginal sensitivity model, f-sensitivity models, and Rosenbaum's sensitivity model; (ii) different treatment types (i.e., binary and continuous); and (iii) different causal queries, including (conditional) average treatment effects and simultaneous effects on multiple outcomes. The generality of NeuralCSA is achieved by learning a latent distribution shift that corresponds to a treatment intervention using two conditional normalizing flows. We provide theoretical guarantees that NeuralCSA is able to infer valid bounds on the causal query of interest and also demonstrate this empirically using both simulated and real-world data.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 40 of its 67 references that are in the map. First posted on arXiv 27 Nov 2023.

Builds on

40 of 67 references.

References · 40

  1. 2015Variational Inference with Normalizing Flowscited by 2,170
  2. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 600
  3. 2019Neural Spline Flowscited by 596
  4. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 478
  5. 2016Learning Representations for Counterfactual Inferencecited by 329
  6. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 287
  7. 2019Learning Likelihoods with Conditional Normalizing Flowscited by 172
  8. 2021Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithmscited by 144
  9. 2019MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-IIIcited by 122
  10. 2021The Causal-Neural Connection: Expressiveness, Learnability, and Inferencecited by 98
  11. 2018Confounding-Robust Policy Improvementcited by 76
  12. 2021Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approachcited by 62
28 more
  1. 2022Neural Causal Models for Counterfactual Identification and Estimationcited by 57
  2. 2018Interval Estimation of Individual-Level Causal Effects Under Unobserved Confoundingcited by 51
  3. 2021Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confoundingcited by 50
  4. 2021Semiparametric counterfactual density estimationcited by 42
  5. 2021An Automated Approach to Causal Inference in Discrete Settingscited by 42
  6. 2020Invertible Generative Modeling using Linear Rational Splinescited by 40
  7. 2021Conformal Sensitivity Analysis for Individual Treatment Effectscited by 40
  8. 2019The Sensitivity of Counterfactual Fairness to Unmeasured Confoundingcited by 35
  9. 2017Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrapcited by 33
  10. 2018Counterfactual Mean Embeddingscited by 32
  11. 2021Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancingcited by 32
  12. 2022Estimating average causal effects from patient trajectoriescited by 29
  13. 2020A Class of Algorithms for General Instrumental Variable Modelscited by 28
  14. 2021Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confoundingcited by 28
  15. 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confoundingcited by 28
  16. 2022Stochastic Causal Programming for Bounding Treatment Effectscited by 25
  17. 2023Sharp Bounds for Generalized Causal Sensitivity Analysiscited by 24
  18. 2022Normalizing Flows for Interventional Density Estimationcited by 21
  19. 2022Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventionscited by 20
  20. 2021Generalizing Off-Policy Learning under Sample Selection Biascited by 16
  21. 2023Reliable Off-Policy Learning for Dosage Combinationscited by 15
  22. 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Modelcited by 14
  23. 2022Sensitivity analysis under the f-sensitivity models: a distributional robustness perspectivecited by 12
  24. 2022Sensitivity Analysis for Marginal Structural Modelscited by 10
  25. 2022Partial Identification of Dose Responses with Hidden Confounderscited by 7
  26. 2023Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confoundingcited by 7
  27. 2021Interpretable Sensitivity Analysis for Balancing Weightscited by 5
  28. 2020Sharpening the Rosenbaum Sensitivity Bounds to Address Concerns About Interactions Between Observed and Unobserved Covariatescited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 7; 2025: 2; 2026: 7.

17 papers.

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  1. 2024Conformal Prediction for Causal Effects of Continuous Treatmentscited by 19
  2. 2024Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environmentscited by 11
  3. 2023Causal Fairness under Unobserved Confounding: A Neural Sensitivity Frameworkcited by 10
  4. 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learnercited by 10
  5. 2024Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challengescited by 8
  6. 2024Causal Machine Learning for Cost-Effective Allocation of Development Aidcited by 6
  7. 2025Efficient and Sharp Off-Policy Learning under Unobserved Confoundingcited by 6
  8. 2024Consistency of Neural Causal Partial Identificationcited by 5
  9. 2026Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networkscited by 3
  10. 2025Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoringcited by 1
  11. 2026Causal Inference on Networks under Misspecified Exposure Mappings: A Partial Identification Frameworkcited by 1
  12. 2024Teleporter Theory: A General and Simple Approach for Modeling Cross-World Counterfactual Causalitycited by 0
5 more
  1. 2026Uncertainty-aware Causal Decision Making via Effect Bound Decompositioncited by 0
  2. 2026How Wrong Can Your Counterfactual Be? Quantifying Confounding Bias for Continuous Treatments without a Control Groupcited by 0
  3. 2026Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networkscited by 0
  4. 2026ConfoundingSHAP: Quantifying confounding strength in causal inferencecited by 0
  5. 2026What You Observe Determines How You Identify Causal Effects: Evaluating Causal Models across Observational Viewscited by 0