mlsanityData to 14 Oct 2026

Sharp Bounds for Generalized Causal Sensitivity Analysis

arXiv 2305.16988 ↗First posted 26 May 2023cs.LG cs.AIcited by 24

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Abstract (arXiv)

Causal inference from observational data is crucial for many disciplines such as medicine and economics. However, sharp bounds for causal effects under relaxations of the unconfoundedness assumption (causal sensitivity analysis) are subject to ongoing research. So far, works with sharp bounds are restricted to fairly simple settings (e.g., a single binary treatment). In this paper, we propose a unified framework for causal sensitivity analysis under unobserved confounding in various settings. For this, we propose a flexible generalization of the marginal sensitivity model (MSM) and then derive sharp bounds for a large class of causal effects. This includes (conditional) average treatment effects, effects for mediation analysis and path analysis, and distributional effects. Furthermore, our sensitivity model is applicable to discrete, continuous, and time-varying treatments. It allows us to interpret the partial identification problem under unobserved confounding as a distribution shift in the latent confounders while evaluating the causal effect of interest. In the special case of a single binary treatment, our bounds for (conditional) average treatment effects coincide with recent optimality results for causal sensitivity analysis. Finally, we propose a scalable algorithm to estimate our sharp bounds from observational data.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). Builds on 48 of its 83 references that are in the map. First posted on arXiv 26 May 2023.

Builds on

48 of 83 references.

References · 48

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
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36 more
  1. 2022Causal Transformer for Estimating Counterfactual Outcomescited by 123
  2. 2019Kernel Instrumental Variable Regressioncited by 118
  3. 2021The Causal-Neural Connection: Expressiveness, Learnability, and Inferencecited by 98
  4. 2019Deep Generalized Method of Moments for Instrumental Variable Analysiscited by 88
  5. 2020Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networkscited by 78
  6. 2018Confounding-Robust Policy Improvementcited by 76
  7. 2020Learning Deep Features in Instrumental Variable Regressioncited by 66
  8. 2021Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approachcited by 62
  9. 2022Neural Causal Models for Counterfactual Identification and Estimationcited by 57
  10. 2018Interval Estimation of Individual-Level Causal Effects Under Unobserved Confoundingcited by 51
  11. 2021Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confoundingcited by 50
  12. 2018Learning Optimal Fair Policiescited by 49
  13. 2021Semiparametric counterfactual density estimationcited by 42
  14. 2021An Automated Approach to Causal Inference in Discrete Settingscited by 42
  15. 2021Nested Counterfactual Identification from Arbitrary Surrogate Experimentscited by 38
  16. 2017Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrapcited by 33
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  20. 2022Estimating average causal effects from patient trajectoriescited by 29
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  22. 2021Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confoundingcited by 28
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Citing papers per year: 2023: 6; 2024: 9; 2025: 5; 2026: 4.

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  1. 2024Causal machine learning for predicting treatment outcomescited by 74
  2. 2025Foundation Models for Causal Inference via Prior-Data Fitted Networkscited by 32
  3. 2023Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimationcited by 20
  4. 2024Model-agnostic meta-learners for estimating heterogeneous treatment effects over timecited by 19
  5. 2023A Neural Framework for Generalized Causal Sensitivity Analysiscited by 17
  6. 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Modelcited by 14
  7. 2024Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environmentscited by 11
  8. 2023Causal Fairness under Unobserved Confounding: A Neural Sensitivity Frameworkcited by 10
  9. 2024IGC-Net for conditional average potential outcome estimation over timecited by 10
  10. 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learnercited by 10
  11. 2024Causal Machine Learning for Cost-Effective Allocation of Development Aidcited by 6
  12. 2024Constructing Confidence Intervals for Average Treatment Effects from Multiple Datasetscited by 6
12 more
  1. 2025Efficient and Sharp Off-Policy Learning under Unobserved Confoundingcited by 6
  2. 2023Data-Driven Allocation of Preventive Care With Application to Diabetes Mellitus Type IIcited by 5
  3. 2024Targeted Sequential Indirect Experiment Designcited by 4
  4. 2025Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inferencecited by 3
  5. 2026Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networkscited by 3
  6. 2024Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Datacited by 2
  7. 2026Causal methods for LLM development and evaluationcited by 2
  8. 2025Your Assumed DAG is Wrong and Here's How To Deal With Itcited by 1
  9. 2025Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoringcited by 1
  10. 2026Causal Inference on Networks under Misspecified Exposure Mappings: A Partial Identification Frameworkcited by 1
  11. 2023Analyzing User Characteristics of Hate Speech Spreaders on Social Mediacited by 0
  12. 2026ConfoundingSHAP: Quantifying confounding strength in causal inferencecited by 0