mlsanityData to 7 Oct 2026

RealCause: Realistic Causal Inference Benchmarking

arXiv 2011.15007 ↗First posted 30 Nov 2020cs.LG cs.AI stat.MLcited by 44

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Abstract (arXiv)

There are many different causal effect estimators in causal inference. However, it is unclear how to choose between these estimators because there is no ground-truth for causal effects. A commonly used option is to simulate synthetic data, where the ground-truth is known. However, the best causal estimators on synthetic data are unlikely to be the best causal estimators on real data. An ideal benchmark for causal estimators would both (a) yield ground-truth values of the causal effects and (b) be representative of real data. Using flexible generative models, we provide a benchmark that both yields ground-truth and is realistic. Using this benchmark, we evaluate over 1500 different causal estimators and provide evidence that it is rational to choose hyperparameters for causal estimators using predictive metrics.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Nov 2020.

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Citing papers per year: 2021: 6; 2022: 6; 2023: 10; 2024: 4; 2025: 11; 2026: 7.

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  1. 2025TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Modelscited by 115
  2. 2025Do-PFN: In-Context Learning for Causal Effect Estimationcited by 46
  3. 2025CausalPFN: Amortized Causal Effect Estimation via In-Context Learningcited by 43
  4. 2021Causal Effect Inference for Structured Treatmentscited by 38
  5. 2023Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Datacited by 31
  6. 2022CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Datacited by 29
  7. 2022Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimationcited by 25
  8. 2025CausalRivers -- Scaling up benchmarking of causal discovery for real-world time-seriescited by 24
  9. 2021DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptionscited by 20
  10. 2022Validating Causal Inference Methodscited by 20
  11. 2022Membership Inference Attacks and Generalization: A Causal Perspectivecited by 17
  12. 2023Counterfactual Generative Models for Time-Varying Treatmentscited by 17
32 more
  1. 2023Towards Causal Foundation Model: on Duality between Causal Inference and Attentioncited by 17
  2. 2021Generating Synthetic Text Data to Evaluate Causal Inference Methodscited by 9
  3. 2024Marginal Causal Flows for Validation and Inferencecited by 8
  4. 2023RCT Rejection Sampling for Causal Estimation Evaluationcited by 7
  5. 2022Quantitative probing: Validating causal models using quantitative domain knowledgecited by 6
  6. 2023The Challenges of Hyperparameter Tuning for Accurate Causal Effect Estimationcited by 6
  7. 2023Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learningcited by 6
  8. 2025Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoptioncited by 6
  9. 2026Use What You Know: Causal Foundation Models with Partial Graphscited by 6
  10. 2021Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimatorscited by 4
  11. 2023Prescriptive Process Monitoring Under Resource Constraints: A Reinforcement Learning Approachcited by 4
  12. 2024Language Models as Causal Effect Generatorscited by 4
  13. 2025SimBank: from Simulation to Solution in Prescriptive Process Monitoringcited by 4
  14. 2026Causal Foundation Models with Continuous Treatmentscited by 4
  15. 2023Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimationcited by 3
  16. 2023SpaCE: The Spatial Confounding Environmentcited by 3
  17. 2022Undersmoothing Causal Estimators with Generative Treescited by 2
  18. 2024DAG-aware Transformer for Causal Effect Estimationcited by 2
  19. 2025Frugal, Flexible, Faithful: Causal Data Simulation via Frengressioncited by 2
  20. 2025ProCause: Generating Counterfactual Outcomes to Evaluate Prescriptive Process Monitoring Methodscited by 2
  21. 2025Synthetic Counterfactual Labels for Efficient Conformal Counterfactual Inferencecited by 2
  22. 2023Timing Process Interventions with Causal Inference and Reinforcement Learningcited by 1
  23. 2024Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimationcited by 1
  24. 2021A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patientscited by 0
  25. 2021ADCB: An Alzheimer's disease benchmark for evaluating observational estimators of causal effectscited by 0
  26. 2025Improving Generative Methods for Causal Evaluation via Simulation-Based Inferencecited by 0
  27. 2025Reliable Selection of Heterogeneous Treatment Effect Estimatorscited by 0
  28. 2026Optimal and Structure-Adaptive CATE Estimation with Kernel Ridge Regressioncited by 0
  29. 2026Controllable Generative Sandbox for Causal Inferencecited by 0
  30. 2026Causal Foundation Modelscited by 0
  31. 2026Splitting the Difference: Interpretable Causal Forests for Treatment Effect Heterogeneity and Biascited by 0
  32. 2026What You Observe Determines How You Identify Causal Effects: Evaluating Causal Models across Observational Viewscited by 0