Hasso-Plattner-Institut
Prof. Dr. h.c. Hasso Plattner
  
 

Johannes Huegle

Research Assistant, PhD Student

 Phone:+49 (331) 5509-1320
 Fax:+49 (331) 5509-579
 Email:johannes.huegle(at)hpi.de
 Address:August-Bebel-Str. 88, 14482 Potsdam
 Room:V-2.05 (Campus II)
 Profiles:XING, LinkedIn

Research

Causal Structure Learning from Heterogeneous 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 to 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.

Publications

  • Schmidt, C., Huegle, J.: Towards a GPU-Accelerated Causal Inference.HPI Future SOC Lab - Proceedings 2017. pp. 187-194 (2020).
     
  • Schmidt, C., Huegle, J., Horschig, S., Uflacker, M.: Out-of-Core GPU-Accelerated Causal Structure Learning.Algorithms and Architectures for Parallel Processing. ICA3PP 2019. p. 89--104. Springer International Publishing (2020).
     
  • 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. p. 59--77 (2019).
     
  • Hesse, G., Matthies, C., Glass, K., Huegle, J., Uflacker, M.: Quantitative Impact Evaluation of an Abstraction Layer for Data Stream Processing Systems.2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). pp. 1381-1392 (2019).
     
  • 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).
     
  • 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. p. 19:1--19:10. ACM, New York, NY, USA (2018).