mlsanityData to 7 Oct 2026

CausalPlayground: Addressing Data-Generation Requirements in Cutting-Edge Causality Research

arXiv 2405.13092 ↗First posted 21 May 2024cs.AI cs.LGcited by 4

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Abstract (arXiv)

Research on causal effects often relies on synthetic data due to the scarcity of real-world datasets with ground-truth effects. Since current data-generating tools do not always meet all requirements for state-of-the-art research, ad-hoc methods are often employed. This leads to heterogeneity among datasets and delays research progress. We address the shortcomings of current data-generating libraries by introducing CausalPlayground, a Python library that provides a standardized platform for generating, sampling, and sharing structural causal models (SCMs). CausalPlayground offers fine-grained control over SCMs, interventions, and the generation of datasets of SCMs for learning and quantitative research. Furthermore, by integrating with Gymnasium, the standard framework for reinforcement learning (RL) environments, we enable online interaction with the SCMs. Overall, by introducing CausalPlayground we aim to foster more efficient and comparable research in the field. All code and API documentation is available at https://github.com/sa-and/CausalPlayground.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 16 of its 36 references that are in the map. First posted on arXiv 21 May 2024.

Builds on

16 of 36 references.

References · 16

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  2. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  3. 2019Causal Discovery with Reinforcement Learningcited by 145
  4. 2022CITRIS: Causal Identifiability from Temporal Intervened Sequencescited by 132
  5. 2020DoWhy: An End-to-End Library for Causal Inferencecited by 92
  6. 2022Amortized Inference for Causal Structure Learningcited by 89
  7. 2021gCastle: A Python Toolbox for Causal Discoverycited by 60
  8. 2023Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Networkcited by 50
  9. 2019Causal Discovery Toolbox: Uncover causal relationships in Pythoncited by 49
  10. 2023BISCUIT: Causal Representation Learning from Binary Interactionscited by 38
  11. 2022Active Bayesian Causal Inferencecited by 34
  12. 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
4 more
  1. 2021Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Datacited by 22
  2. 2022A Meta-Reinforcement Learning Algorithm for Causal Discoverycited by 11
  3. 2023Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Searchcited by 8
  4. 2024CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learningcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Do-PFN: In-Context Learning for Causal Effect Estimationcited by 46
  2. 2025ACTIVA: Amortized Causal Effect Estimation via Transformer-based Variational Autoencodercited by 1
  3. 2025Integrating a Causal Foundation Model into a Prescriptive Maintenance Framework for Optimising Production-Line OEEcited by 0
  4. 2026Amortized Interventional Forecasting for Multivariate CIR Processescited by 0