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

Johannes Huegle

Research Assistant, PhD Student

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

Research

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

Lectures:

  • 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)

Seminars:

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)

 

Education

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.

Publications

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