mlsanityData to 14 Oct 2026

textttcausalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery

arXiv 2306.10816 ↗First posted 19 Jun 2023stat.ML cs.LG stat.MEcited by 18

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Abstract (arXiv)

Algorithms for causal discovery have recently undergone rapid advances and increasingly draw on flexible nonparametric methods to process complex data. With these advances comes a need for adequate empirical validation of the causal relationships learned by different algorithms. However, for most real data sources true causal relations remain unknown. This issue is further compounded by privacy concerns surrounding the release of suitable high-quality data. To help address these challenges, we gather a complex dataset comprising measurements from an assembly line in a manufacturing context. This line consists of numerous physical processes for which we are able to provide ground truth causal relationships on the basis of a detailed study of the underlying physics. We use the assembly line data and associated ground truth information to build a system for generation of semisynthetic manufacturing data that supports benchmarking of causal discovery methods. To accomplish this, we employ distributional random forests in order to flexibly estimate and represent conditional distributions that may be combined into joint distributions that strictly adhere to a causal model over the observed variables. The estimated conditionals and tools for data generation are made available in our Python library textttcausalAssembly. Using the library, we showcase how to benchmark several well-known causal discovery algorithms.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 24 of its 61 references that are in the map. First posted on arXiv 19 Jun 2023.

Builds on

24 of 61 references.

References · 24

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  2. 2013Causal Discovery with Continuous Additive Noise Modelscited by 255
  3. 2019Gradient-Based Neural DAG Learningcited by 227
  4. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 211
  5. 2019Learning Sparse Nonparametric DAGscited by 207
  6. 2020On the Role of Sparsity and DAG Constraints for Learning Linear DAGscited by 191
  7. 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 178
  8. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 161
  9. 2022Score matching enables causal discovery of nonlinear additive noise modelscited by 111
  10. 2017Causal Structure Learningcited by 84
  11. 2016Structure Learning in Graphical Modelingcited by 66
  12. 2019Causal structure based root cause analysis of outlierscited by 65
12 more
  1. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
  2. 2018On Causal Discovery with Equal Variance Assumptioncited by 59
  3. 2021DAGs with No Curl: An Efficient DAG Structure Learning Approachcited by 53
  4. 2023Scalable Causal Discovery with Score Matchingcited by 45
  5. 2020Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regressioncited by 37
  6. 2019The Case for Evaluating Causal Models Using Interventional Measures and Empirical Datacited by 30
  7. 2020Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy datacited by 29
  8. 2022Truncated Matrix Power Iteration for Differentiable DAG Learningcited by 23
  9. 2022Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventionscited by 12
  10. 2021Benchpress: A Scalable and Versatile Workflow for Benchmarking Structure Learning Algorithmscited by 11
  11. 2020Evaluation of Causal Structure Learning Algorithms via Risk Estimationcited by 8
  12. 2023The Structurally Complex with Additive Parent Causality (SCARY) Datasetcited by 2

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

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  1. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 18
  2. 2024The Causal Chambers: Real Physical Systems as a Testbed for AI Methodologycited by 12
  3. 2025Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discoverycited by 8
  4. 2025CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal modelscited by 8
  5. 2025Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoptioncited by 6
  6. 2025Nonlinear Causal Discovery for Grouped Datacited by 5
  7. 2025CausalMan: A physics-based simulator for large-scale causalitycited by 4
  8. 2025Embracing Discrete Search: A Reasonable Approach to Causal Structure Learningcited by 4
  9. 2024Counterfactual-based Root Cause Analysis for Dynamical Systemscited by 2
  10. 2025A Fast Kernel-based Conditional Independence test with Application to Causal Discoverycited by 2
  11. 2024Anwendung von Causal-Discovery-Algorithmen zur Root-Cause-Analyse in der Fahrzeugmontagecited by 1
  12. 2026Order-based Causal Discovery for Multistage Processescited by 1
6 more
  1. 2024Kernel-Based Differentiable Learning of Non-Parametric Directed Acyclic Graphical Modelscited by 0
  2. 2024A method to benchmark high-dimensional process drift detectioncited by 0
  3. 2026A Topological Sorting Criterion for Random Causal Directed Acyclic Graphscited by 0
  4. 2026The Good, the Bad, and the Ugly of Markov Boundary for Tabular Predictioncited by 0
  5. 2026Falsifying Causal Graphs With Outlier Eventscited by 0
  6. 2026CausalArena: Benchmarking Causal Discovery in the Foundation Model Eracited by 0