CausalBench: Causal Learning Research Streamlined

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Schedule

The tutorial will take place at KDD'25, on Monday, August 4, 8:00 AM – 11:00 AM, in Toronto Convention Centre, Room 707.

Tutorial

In this 3-hour long tutorial, we will explore the field of Causality, the state of the art, difficulties in benchmarking studies, and learn how to utilize CausalBench to facilitate comparative and transparent benchmarking. Read the tutorial paper here.

Tutorial Abstract

Recent advances in causal machine learning introduced a plethora of new causal discovery and causal inference models to tackle decision support problems. Yet, these models exhibit different performance when they train on different data, and even different hardware/software platforms, making it challenging for users to select the appropriate setup pertinent to their specific problem instance. The situation is complicated by the fact that, until recently, the field lacked a unified, publicly available, and configurable platform that supports all major causal inference tasks, including causal discovery, causal effect estimation, and causal inference. CausalBench is a comprehensive benchmarking tool for causal machine learning that facilitates accurate and reproducible benchmarking of causal models across metrics and deployment contexts and helps users to select the most appropriate set up (such as hyper-parameter configuration) for the specific problem setting. This tutorial is intended to familiarize attendees from diverse backgrounds, who are interested in causal learning models and with the capabilities of CausalBench. The tutorial begins with an introduction to "causality" and causal machine learning, and then provides hands-on experience with CausalBench to equip attendees with the knowledge necessary to utilize CausalBench for their causal learning problems.

Tutorial Material

We provide several tutorial materials below for your convenience and reference. These materials may be updated in the future to provide the best CausalBench experience.

Slides

You may access the slides here.

Quickstart file (Google Colab)

For your convenience, we have provided two Python Notebook files that can be run on Google Colab.

Presenters
Ahmet Kapkiç Pratanu Mandal Abhinav Gorantla Huan Liu
Arizona State University Arizona State University Arizona State University Arizona State University

Contributors
Shu Wan Ertuğrul Çoban Paras Sheth K. Selçuk Candan
Arizona State University Arizona State University Amazon Arizona State University

Program Outline
Time Content
08:00-08:05 Introduction to the Tutorial
08:05-08:25 Introduction to CausalBench
08:25-08:55 Introduction to Causality and Causal Learning
08:55-09:30 Delve into the CasualBench framework to create and execute benchmarks
09:30-10:00 Coffee break
10:00-10:10 Shorter introduction to CausalBench
10:10-10:35 Explore published benchmarks and reproduce experiments
10:35-10:50 Gain further insights using Causal Analysis and Recommendations
10:50-11:00 CausalBench: What's Next?

Covered Topics

  1. CausalBench
    • Introduction to CausalBench
    • Installing CausalBench and setting up a benchmark
    • Benchmark contexts: details and configuration files
    • Creating and executing a benchmark context
    • Analyzing and interpreting the results
    • Challenges ahead and contributing to CausalBench
  2. Causality
    • Introduction to causality [6, 7]
    • Models for representing causal knowledge [3, 5]
    • State-of-the art in causal discovery and causal effect estimation [1, 2, 8]

Documentation

You can access CausalBench documentation at docs.causalbench.org, which is the acting knowledge base for CausalBench.

References

  1. Azad, F. T., Candan, K. S., Kapkiç, A., Li, M.-L., Liu, H., Mandal, P., Sheth, P., Arslan, B., Chowell-Puente, G., Sabo, J., et al. (vision paper) a vision for spatio-causal situation awareness, forecasting, and planning. ACM Transactions on Spatial Algorithms and Systems 10, 2 (2024), 1–42.
  2. Guo, R., Cheng, L., Li, J., Hahn, P. R., and Liu, H. A survey of learning causality with data: Problems and methods. ACM Computing Surveys (CSUR) 53, 4 (2020), 1–37.
  3. Hoyle, R. H. Handbook of structural equation modeling. Guilford press, 2012.
  4. Kapkiç, A., Mandal, P., Wan, S., Sheth, P., Gorantla, A., Choi, Y., Liu, H., and Candan, K. S. Introducing causalbench: A flexible benchmark framework for causal analysis and machine learning. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (2024), pp. 5220–5224.
  5. Pearl, J. Graphs, causality, and structural equation models. Sociological Methods & Research 27, 2 (1998), 226–284.
  6. Pearl, J. Causality. Cambridge university press, 2009.
  7. Pearl, J., Glymour, M., and Jewell, N. P. Causal inference in statistics: A primer. John Wiley & Sons, 2016.
  8. Wan, S., Shah, R., Deng, Q., Sabo, J., Liu, H., and Candan, K. S. Spatio-temporal causal learning for streamflow forecasting. In 2024 IEEE International Conference on Big Data (BigData) (2024), IEEE, pp. 6161–6170.

Contact

For questions regarding tutorial, please contact us at support@causalbench.org or akapkic@asu.edu.

Acknowledgments

We thank all the contributors and the community for their continuous support and feedback in making CausalBench a reliable and valuable resource for causal learning research. This research is funded by NSF Grant 2311716, "CausalBench: A Cyberinfrastructure for Causal-Learning Benchmarking for Efficacy, Reproducibility, and Scientific Collaboration", and NSF Grants #2230748, "PIRE: Building Decarbonization via AI-empowered District Heat Pump Systems", #2412115, "PIPP Phase II: Analysis and Prediction of Pandemic Expansion (APPEX)" and USACE #GR40695, "Designing nature to enhance resilience of built infrastructure in western US landscapes".