mlsanityData to 7 Oct 2026

Stochastic Causal Programming for Bounding Treatment Effects

arXiv 2202.10806 ↗First posted 22 Feb 2022stat.ML cs.LGcited by 25

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Abstract (arXiv)

Causal effect estimation is important for many tasks in the natural and social sciences. We design algorithms for the continuous partial identification problem: bounding the effects of multivariate, continuous treatments when unmeasured confounding makes identification impossible. Specifically, we cast causal effects as objective functions within a constrained optimization problem, and minimize/maximize these functions to obtain bounds. We combine flexible learning algorithms with Monte Carlo methods to implement a family of solutions under the name of stochastic causal programming. In particular, we show how the generic framework can be efficiently formulated in settings where auxiliary variables are clustered into pre-treatment and post-treatment sets, where no fine-grained causal graph can be easily specified. In these settings, we can avoid the need for fully specifying the distribution family of hidden common causes. Monte Carlo computation is also much simplified, leading to algorithms which are more computationally stable against alternatives.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 24 of its 74 references that are in the map. First posted on arXiv 22 Feb 2022.

Builds on

24 of 74 references.

References · 24

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12 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 9; 2024: 6; 2025: 3; 2026: 4.

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  1. 2023Sharp Bounds for Generalized Causal Sensitivity Analysiscited by 24
  2. 2022Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventionscited by 20
  3. 2022Partial Identification of Treatment Effects with Implicit Generative Modelscited by 17
  4. 2023A Neural Framework for Generalized Causal Sensitivity Analysiscited by 17
  5. 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Modelcited by 14
  6. 2024Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environmentscited by 11
  7. 2023Towards Bounding Causal Effects under Markov Equivalencecited by 8
  8. 2022Partial Identification of Dose Responses with Hidden Confounderscited by 7
  9. 2023AI for Science: An Emerging Agendacited by 5
  10. 2023Sequential Underspecified Instrument Selection for Cause-Effect Estimationcited by 4
  11. 2024Targeted Sequential Indirect Experiment Designcited by 4
  12. 2024Learning Representations of Instruments for Partial Identification of Treatment Effectscited by 4
13 more
  1. 2023Bounding probabilities of causation through the causal marginal problemcited by 3
  2. 2023Approximate Causal Effect Identification under Weak Confoundingcited by 3
  3. 2024Counterfactual Fairness Is Not Demographic Parity, and Other Observationscited by 3
  4. 2024Consistency of Neural Causal Partial Identificationcited by 3
  5. 2026Information-Theoretic Causal Bounds under Unmeasured Confoundingcited by 2
  6. 2026IV-ICL: Bounding Causal Effects with Instrumental Variables via In-Context Learningcited by 2
  7. 2026Foundation Models for Partial Causal Identificationcited by 2
  8. 2023Tightening Bounds on Probabilities of Causation By Merging Datasetscited by 1
  9. 2025Your Assumed DAG is Wrong and Here's How To Deal With Itcited by 1
  10. 2025Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoringcited by 1
  11. 2024Bounding Causal Effects with Leaky Instrumentscited by 0
  12. 2025Quantum oracles give an advantage for identifying classical counterfactualscited by 0
  13. 2026Causal Variational Deep Embedding: A Family of Interventional Generators for Confounded Imagescited by 0