mlsanityData to 7 Oct 2026

Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data

arXiv 2111.02275 ↗First posted 3 Nov 2021cs.LG stat.MLcited by 27

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Abstract (arXiv)

Estimating personalized treatment effects from high-dimensional observational data is essential in situations where experimental designs are infeasible, unethical, or expensive. Existing approaches rely on fitting deep models on outcomes observed for treated and control populations. However, when measuring individual outcomes is costly, as is the case of a tumor biopsy, a sample-efficient strategy for acquiring each result is required. Deep Bayesian active learning provides a framework for efficient data acquisition by selecting points with high uncertainty. However, existing methods bias training data acquisition towards regions of non-overlapping support between the treated and control populations. These are not sample-efficient because the treatment effect is not identifiable in such regions. We introduce causal, Bayesian acquisition functions grounded in information theory that bias data acquisition towards regions with overlapping support to maximize sample efficiency for learning personalized treatment effects. We demonstrate the performance of the proposed acquisition strategies on synthetic and semi-synthetic datasets IHDP and CMNIST and their extensions, which aim to simulate common dataset biases and pathologies.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 28 of its 66 references that are in the map. First posted on arXiv 3 Nov 2021.

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28 of 66 references.

References · 28

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16 more
  1. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 285
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  3. 2017Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processescited by 156
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  16. 2021PowerEvaluationBALD: Efficient Evaluation-Oriented Deep (Bayesian) Active Learning with Stochastic Acquisition Functionscited by 4

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Citing papers per year: 2022: 5; 2023: 7; 2024: 7; 2025: 6; 2026: 2.

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  1. 2024A Survey on Deep Active Learning: Recent Advances and New Frontierscited by 53
  2. 2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
  3. 2022Active Learning for Optimal Intervention Design in Causal Modelscited by 30
  4. 2023Black-Box Batch Active Learning for Regressioncited by 12
  5. 2022Sample Constrained Treatment Effect Estimationcited by 7
  6. 2023Learning Complementary Policies for Human-AI Teamscited by 7
  7. 2023Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confoundingcited by 7
  8. 2024Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitionscited by 7
  9. 2024Improving Interpretability of Deep Active Learning for Flood Inundation Mapping Through Class Ambiguity Indices Using Multi-spectral Satellite Imagerycited by 6
  10. 2024Bayesian Active Learning for Semantic Segmentationcited by 5
  11. 2022Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Makingcited by 4
  12. 2023Online Decision Mediationcited by 4
15 more
  1. 2024Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimationcited by 3
  2. 2025Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutionscited by 3
  3. 2025Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspectivecited by 3
  4. 2025X-Factor: Quality Is a Dataset-Intrinsic Propertycited by 3
  5. 2023Task-specific experimental design for treatment effect estimationcited by 2
  6. 2023Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning?cited by 2
  7. 2024Bayesian Active Learning for Censored Regressioncited by 2
  8. 2024Is merging worth it? Securely evaluating the information gain for causal dataset acquisitioncited by 2
  9. 2025Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimationcited by 2
  10. 2023Learning Objective-Specific Active Learning Strategies with Attentive Neural Processescited by 1
  11. 2025ActiveCQ: Active Estimation of Causal Quantitiescited by 1
  12. 2026Observationally Informed Adaptive Causal Experimental Designcited by 1
  13. 2022Building Trust: Lessons from the Technion-Rambam Machine Learning in Healthcare Datathon Eventcited by 0
  14. 2025Matched-Pair Experimental Design with Active Learningcited by 0
  15. 2026Treatment Effect Estimation with Differentiated Networked Effect on Graph Datacited by 0