Prof. Dr. h.c. mult. Hasso Plattner

Johannes Huegle

Research Assistant, PhD Student

Phone:+49 (331) 5509-1320
Address:August-Bebel-Str. 88, 14482 Potsdam
Room:V-2.05 (Campus II)
Profiles:ResearchGate, Google ScholarXING, LinkedIn


Causal Structure Learning from Mixed and Nonlinear Data

Causal structure learning (CSL), i.e., the derivation of the causal graphical model representing the functional relationships between variables of a system from observational data, is crucial to many domains. However, while current methods of CSL assume the same data type or a specific family of functional relationships, most real-world scenarios incorporate heterogeneous data, e.g., mixed data. Moreover, as the underlying functional causal model and corresponding consistent model-based dependence measures are mostly unknown in advance the selection of inappropriate methods for CSL yields incorrect results.

As part of our research on Data-Driven Causal Inference we address these challenges through the introduction of information theoretic statistical methods and the development of a modular pipeline for experimental evaluation of CSL from observational data. We demonstrate the applicability of our approaches on both synthetic and real-world settings with regard to the consistency of the estimated underlying causal structures.

Keywords: Causal Structure Learning, Causal Inference, Machine Learning in Real-World Scenarios, Computational Statistics

Current Projects

  • Data-Driven Causal Inference
    • Information-Theoretic Causal Structure Learning from Mixed and Nonlinear Data
    • MPCSL - A Modular Pipeline for Causal Structure Learning
  • Application Scenarios with Cooperation Partners
    • Mechanical Engineering
    • Automotive Production

Teaching Activities


  • Causal Inference - Theory and Applications in Enterprise Computing (SS18, SS19, SS20)
  • Trends and Concepts in the Software Industry I (SS17)
  • Trends and Concepts in the Software Industry II (WS16/17)


Master Projects:

  • GPU-Accelerated Causal Structure Learning (WS21/22)
  • A Benchmark Suite for Causal Inference or “How to model a complex world?” (WS20/21)
  • Causal Reasoning on Enterprise Data (WS19/20)
  • Design and Implementation of a Causal Inference Pipeline (WS18/19)

Bachelor Projects:

(Co-) Supervised Master's Theses:

  • Causal Structure Learning for Car Manufacturing using Background Knowledge (Tobias Nack)
  • Parallel Execution Strategies for Constraint-Based Causal Structure Learning with an Entropy-Based Conditional Independence Test (Constantin Lange)
  • Constraint-based Causal Structure Learning of Gene Regulatory Networks (Philipp Bode)
  • An Entropy-Based Conditional Independence Test for Causal Structure Learning from Heterogeneous Data (Daniel Thevessen)
  • A Scalable GPU-Accelerated Causal Structure Learning Algorithm (Siegfried Horschig)



Master's Degree in Mathematics, Heidelberg University, 2017, Focus: Statistics and Probability Theory, Thesis: "Asymptotics for Directed Interaction Networks based on Counting Process Theory"

Bachelor's Degree in Mathematics, Heidelberg University, 2014, Focus: Statistics and Probability Theory, Thesis "Pricing of Derivatives in the Binomial Model"

Selected Talks & Presentations

  • "Causal Discovery in Practice: Tooling & Non-Parametric Conditional Independence Testing for Causal Discovery" European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) - PhD Forum, 2023.
  • "Causal structure learning for heterogeneous data characteristics of real-world scenarios", European Causal Inference Meeting (EuroCIM), 2021.
  • "An Information-Theoretic Approach on Causal Structure Learning within Heterogeneous Data Characteristics of Real-World Scenarios", SIAM International Conference on Data Mining (SDM) - Doctoral Forum, 2021.
  • "Probabilistic Machine Learning - Introduction to Causal Inference", SAP Innovation Series, 2017.


  • 1.
    Huegle, J., Hagedorn, C., Schlosser, R.: A kNN-based Non-Parametric Conditional Independence Test for Mixed Data and Application in Causal Discovery. ECML-PKDD 2023, accepted (2023).
  • 2.
    Groeneveld, J., Herrmann, J., Mollenhauer, N., Dreessen, L., Bessin, N., Schulze-Tast, J., Kastius, A., Huegle, J., Schlosser, R.: Self-Learning Agents for Recommerce Markets. Business & Information Systems Engineering, accepted. (2023).
  • 3.
    Braun, T., Hurdelhey, B., Meier, D., Tsayun, P., Hagedorn, C., Huegle, J., Schlosser, R.: GPUCSL: GPU-Based Library for Causal Structure Learning. ICDM Open Project Forum. pp. 1236–1239 (2022).
  • 4.
    Hagedorn, C., Lange, C., Huegle, J., Schlosser, R.: GPU Acceleration for Information-theoretic Constraint-based Causal Discovery. In: Le, T.D., Liu, L., Kıcıman, E., Triantafyllou, S., and Liu, H. (eds.) Proceedings of The KDD’22 Workshop on Causal Discovery, Proceedings of Machine Learning Research (PMLR) 185. pp. 30–60 (2022).
  • 5.
    Hagedorn, C., Huegle, J., Schlosser, R.: Understanding Unforeseen Production Downtimes in Manufacturing Processes using Log Data-driven Causal Reasoning. Journal of Intelligent Manufacturing. 33, 2027–2043 (2022).
  • 6.
    Huegle, J., Hagedorn, C., Perscheid, M., Plattner, H.: MPCSL - A Modular Pipeline for Causal Structure Learning. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. pp. 3068–3076. Association for Computing Machinery, New York, NY, USA (2021).
  • 7.
    Huegle, J., Hagedorn, C., Uflacker, M.: Unterstützte Fehlerbehebung durch kausales Strukturwissen in Überwachungssystemen der Automobilfertigung. In: Götz, S., Linsbauer, L., Schaefer, I., and Wortmann, A. (eds.) Software Engineering 2021 Satellite Events, Lecture Notes in Informatics (LNI). Gesellschaft für Informatik, Bonn (2021).
  • 8.
    Hagedorn, C., Huegle, J.: GPU-Accelerated Constraint-Based Causal Structure Learning for Discrete Data. Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). pp. 37–45 (2021).
  • 9.
    Huegle, J.: An Information-Theoretic Approach on Causal Structure Learning for Heterogeneous Data Characteristics of Real-World Scenarios. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. pp. 4891–4892. International Joint Conferences on Artificial Intelligence Organization (2021).
  • 10.
    Hagedorn, C., Huegle, J.: Constraint-Based Causal Structure Learning in Multi-GPU Environments. In: Seidl, T., Fromm, M., and Obermeier, S. (eds.) Proceedings of the LWDA 2021 Workshops: FGWM, KDML, FGWI-BIA, and FGIR, Online, September 1-3, 2021. pp. 106–118. CEUR-WS.org (2021).
  • 11.
    Huegle, J., Hagedorn, C., Boehme, L., Poerschke, M., Umland, J., Schlosser, R.: MANM-CS: Data Generation for Benchmarking Causal Structure Learning from Mixed Discrete-Continuous and Nonlinear Data. WHY-21 @ NeurIPS 2021 (2021).
  • 12.
    Matthies, C., Huegle, J., Dürschmid, T., Teusner, R.: Attitudes, Beliefs, and Development Data Concerning Agile Software Development Practices. In: Felderer, M., Hasselbring, W., Rabiser, R., and Jung, R. (eds.) Software Engineering 2020. pp. 73–74. Gesellschaft für Informatik e.V., Bonn (2020).
  • 13.
    Huegle, J., Hagedorn, C., Uflacker, M.: How Causal Structural Knowledge Adds Decision-Support in Monitoring of Automotive Body Shop Assembly Lines. In: Bessiere, C. (ed.) Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20. pp. 5246–5248. International Joint Conferences on Artificial Intelligence Organization (2020).
  • 14.
    Schmidt, C., Huegle, J.: Towards a GPU-Accelerated Causal Inference. HPI Future SOC Lab - Proceedings 2017. pp. 187–194 (2020).
  • 15.
    Schmidt, C., Huegle, J., Horschig, S., Uflacker, M.: Out-of-Core GPU-Accelerated Causal Structure Learning. Algorithms and Architectures for Parallel Processing. ICA3PP 2019. pp. 89–104. Springer International Publishing (2020).
  • 16.
    Schmidt, C., Huegle, J., Bode, P., Uflacker, M.: Load-Balanced Parallel Constraint-Based Causal Structure Learning on Multi-Core Systems for High-Dimensional Data. SIGKDD Workshop on Causal Discovery. pp. 59–77 (2019).
  • 17.
    Hesse, G., Matthies, C., Glass, K., Huegle, J., Uflacker, M.: Quantitative Impact Evaluation of an Abstraction Layer for Data Stream Processing Systems. IEEE International Conference on Distributed Computing Systems (ICDCS). pp. 1381–1392 (2019).
  • 18.
    Matthies, C., Huegle, J., Dürschmid, T., Teusner, R.: Attitudes, Beliefs, and Development Data Concerning Agile Software Development Practices. 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET). pp. 158–169. IEEE (2019).
  • 19.
    Schmidt, C., Huegle, J., Uflacker, M.: Order-independent constraint-based causal structure learning for gaussian distribution models using GPUs. SSDBM ’18 Proceedings of the 30th International Conference on Scientific and Statistical Database Management. pp. 19:1–19:10. ACM, New York, NY, USA (2018).