We address open challenges in the context of causal structure learning by improvements in both the application of statistical and probabilistic concepts, and GPU-based acceleration to support a utilization in a real-world context.

The knowledge of the causal structures within complex systems is crucial to many domains. For example in manufacturing, where causal structural knowledge constitutes the basis of error avoidance in a production process. While domain experts within the company have enough expertise to identify the most common relationships, they will require support in the context of both, an increasing amount of observational data and the complexity of large systems. This gap can be closed by algorithms of causal structure learning (CSL) that derive the underlying causal structures from observational data, e.g., error messages and inline measurements of a production process.

The questions that motivate most data analysis are not associational but causal in nature. What are the causes and what are the effects of events under observation? Nevertheless, the statistical methods commonly used today to answer those questions are of associational nature. But “Correlation does not imply causation!”, and misinterpretation often results in incorrect deduction. 

In the recent years, causality has grown from a nebulous concept into a mathematical theory. This is largely due to the work of Judea Pearl (2009) who has received the 2011 Turing Award for “fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning”.

Our research in the context of data-driven causal inference concentrates on several workstreams that combined aim to allow an efficient application in real-world scenarios.

Causal Structure Learning:

• Evaluation of real-world scenarios with respect to data characteristics and opportunities of CSL
• Introduction of an information-theoretic approach to improve CSL in the context heterogeneous data

Hard- and Software Acceleration:

• Development of a modular pipeline for experimental evaluation of CSL from observational data 
• Application of Graphics Processing Units (GPU) to develop efficient implementations in heterogeneous computing systems

Together with different cooperation partners we examine how causal inference can be applied in a real-world context.

Manufacturing:

• Identification of relevant involved factors in an industrial manufacturing process
• Derivation of causal structures in complex production processes, e.g., Bachelor Project: Application of Causal Inference in Automotive Production

Medicine:

• Application of CSL in the context of gene expression data

In the context of our research on data-driven causal inference, we provide several master thesis topic areas to work on specific challenges. Should you be interested in any of those topics please feel free to contact the responsible research assistant for further information.

Moreover, we offer positions as student assistants that focus on implementation-related aspects. If you are interested to work in the field of causal structure learning contact us.

Winter Term 2020/21

• A Benchmark Suite for Causal Inference or “How to model a complex world?”

Summer Term 2020

• Lecture: Causal Inference - Theory and Applications in Enterprise Computing

Winter Term 2019/20

• Master Project: Causal Reasoning on Enterprise Data

Summer Term 2019

• Lecture: Causal Inference - Theory and Applications in Enterprise Computing

Winter Term 2018/19

• Master Project: Design and Implementation of a Causal Inference Pipeline

Summer Term 2018

• Bachelor Project: Application of Causal Inference in Automotive Production
• Lecture: Causal Inference – Theory and Applications

Winter Term 2017/18

• Bachelor Project: Application of Causal Inference in Automotive Production

Christopher Hagedorn (geb. Schmidt), Johannes Huegle, Dr. Michael Perscheid