mlsanityData to 7 Oct 2026

Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions

arXiv 2204.10022 ↗First posted 21 Apr 2022cs.LG stat.MLcited by 20

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Abstract (arXiv)

Estimating the effects of continuous-valued interventions from observational data is a critically important task for climate science, healthcare, and economics. Recent work focuses on designing neural network architectures and regularization functions to allow for scalable estimation of average and individual-level dose-response curves from high-dimensional, large-sample data. Such methodologies assume ignorability (observation of all confounding variables) and positivity (observation of all treatment levels for every covariate value describing a set of units), assumptions problematic in the continuous treatment regime. Scalable sensitivity and uncertainty analyses to understand the ignorance induced in causal estimates when these assumptions are relaxed are less studied. Here, we develop a continuous treatment-effect marginal sensitivity model (CMSM) and derive bounds that agree with the observed data and a researcher-defined level of hidden confounding. We introduce a scalable algorithm and uncertainty-aware deep models to derive and estimate these bounds for high-dimensional, large-sample observational data. We work in concert with climate scientists interested in the climatological impacts of human emissions on cloud properties using satellite observations from the past 15 years. This problem is known to be complicated by many unobserved confounders.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 18 of its 89 references that are in the map. First posted on arXiv 21 Apr 2022.

Builds on

18 of 89 references.

References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2016An overview of gradient descent optimization algorithmscited by 1,021
  5. 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 516
  6. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
  7. 2018Tune: A Research Platform for Distributed Model Selection and Trainingcited by 395
  8. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 285
  9. 2019Learning Counterfactual Representations for Estimating Individual Dose-Response Curvescited by 90
  10. 2020Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networkscited by 78
  11. 2021VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatmentscited by 59
  12. 2020Identifying Causal-Effect Inference Failure with Uncertainty-Aware Modelscited by 57
6 more
  1. 2018Interval Estimation of Individual-Level Causal Effects Under Unobserved Confoundingcited by 51
  2. 2021Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confoundingcited by 50
  3. 2017Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrapcited by 33
  4. 2021Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancingcited by 32
  5. 2020A Class of Algorithms for General Instrumental Variable Modelscited by 28
  6. 2022Stochastic Causal Programming for Bounding Treatment Effectscited by 25

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 8; 2024: 4; 2025: 1; 2026: 1.

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  1. 2023Sharp Bounds for Generalized Causal Sensitivity Analysiscited by 24
  2. 2022Normalizing Flows for Interventional Density Estimationcited by 21
  3. 2023A Neural Framework for Generalized Causal Sensitivity Analysiscited by 17
  4. 2023Reliable Off-Policy Learning for Dosage Combinationscited by 15
  5. 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Modelcited by 14
  6. 2023Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounderscited by 12
  7. 2022Deep Causal Learning: Representation, Discovery and Inferencecited by 11
  8. 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learnercited by 10
  9. 2022Partial Identification of Dose Responses with Hidden Confounderscited by 7
  10. 2023Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confoundingcited by 7
  11. 2024Continuous Treatment Effect Estimation Using Gradient Interpolation and Kernel Smoothingcited by 7
  12. 2024Causal Machine Learning for Cost-Effective Allocation of Development Aidcited by 6
8 more
  1. 2022Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventionscited by 5
  2. 2024Detecting and Measuring Confounding Using Causal Mechanism Shiftscited by 3
  3. 2026Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networkscited by 3
  4. 2023PWSHAP: A Path-Wise Explanation Model for Targeted Variablescited by 2
  5. 2023Pretrained Vision Models for Predicting High-Risk Breast Cancer Stagecited by 1
  6. 2025Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoringcited by 1
  7. 2022AODisaggregation: toward global aerosol vertical profilescited by 0
  8. 2022Using uncertainty-aware machine learning models to study aerosol-cloud interactionscited by 0