mlsanityData to 14 Oct 2026

Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation

arXiv 2307.04988 ↗First posted 11 Jul 2023cs.LG stat.MEcited by 3

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Abstract (arXiv)

The practical utility of causality in decision-making is widespread and brought about by the intertwining of causal discovery and causal inference. Nevertheless, a notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference. To address this gap, we evaluate seven established baseline causal discovery methods including a newly proposed method based on GFlowNets, on the downstream task of treatment effect estimation. Through the implementation of a distribution-level evaluation, we offer valuable and unique insights into the efficacy of these causal discovery methods for treatment effect estimation, considering both synthetic and real-world scenarios, as well as low-data scenarios. The results of our study demonstrate that some of the algorithms studied are able to effectively capture a wide range of useful and diverse ATE modes, while some tend to learn many low-probability modes which impacts the (unrelaxed) recall and precision.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 34 of its 63 references that are in the map. First posted on arXiv 11 Jul 2023.

Builds on

34 of 63 references.

References · 34

  1. 2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 392
  2. 2021Transformers Can Do Bayesian Inferencecited by 360
  3. 2021GFlowNet Foundationscited by 290
  4. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 211
  5. 2022Biological Sequence Design with GFlowNetscited by 174
  6. 2015On Wasserstein Two Sample Testing and Related Families of Nonparametric Testscited by 166
  7. 2020Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representationscited by 155
  8. 2022Bayesian Structure Learning with Generative Flow Networkscited by 153
  9. 2022Generative Flow Networks for Discrete Probabilistic Modelingcited by 114
  10. 2020CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learningcited by 109
  11. 2021DiBS: Differentiable Bayesian Structure Learningcited by 106
  12. 2020DoWhy: An End-to-End Library for Causal Inferencecited by 95
22 more
  1. 2022Deep End-to-end Causal Inferencecited by 95
  2. 2017Causal Structure Learningcited by 84
  3. 2021BCD Nets: Scalable Variational Approaches for Bayesian Causal Discoverycited by 68
  4. 2022Causal Machine Learning for Healthcare and Precision Medicinecited by 68
  5. 2016Structure Learning in Graphical Modelingcited by 66
  6. 2023GFlowNets for AI-Driven Scientific Discoverycited by 63
  7. 2019Causal Discovery Toolbox: Uncover causal relationships in Pythoncited by 49
  8. 2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
  9. 2018Causal Discovery in the Presence of Missing Datacited by 47
  10. 2020RealCause: Realistic Causal Inference Benchmarkingcited by 46
  11. 2018Benchmarking Framework for Performance-Evaluation of Causal Inference Analysiscited by 33
  12. 2020Towards Scalable Bayesian Learning of Causal DAGscited by 33
  13. 2022CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Datacited by 29
  14. 2019Atlantic Causal Inference Conference (ACIC) Data Analysis Challenge 2017cited by 24
  15. 2014Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimised Implementations in the bnlearn R Packagecited by 19
  16. 2023Causal Effect Estimation: Recent Advances, Challenges, and Opportunitiescited by 14
  17. 2022CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methodscited by 10
  18. 2021Estimating the Long-Term Effects of Novel Treatmentscited by 8
  19. 2019Online Causal Inference for Advertising in Real-Time Bidding Auctionscited by 6
  20. 2021CausalNLP: A Practical Toolkit for Causal Inference with Textcited by 5
  21. 2019Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Datacited by 4
  22. 2022Causality, Causal Discovery, and Causal Inference in Structural Engineeringcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 1.

3 papers.

  1. 2024Challenges and Considerations in the Evaluation of Bayesian Causal Discoverycited by 5
  2. 2024Enhancing Solution Efficiency in Reinforcement Learning: Leveraging Sub-GFlowNet and Entropy Integrationcited by 0
  3. 2026Moment Matters: Mean and Variance Causal Graph Discovery from Heteroscedastic Observational Datacited by 0