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

Motivation

The emergence of the Internet of Things (IoT) allows for a comprehensive analysis of industrial manufacturing processes. 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 of observed features. This gap can be closed by machine learning algorithms of causal inference that derive the underlying causal relationships between the observed features. 

Background

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”.

Research Goals

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

Causal Structure Learning:

• Allow for more flexible learning algorithms
• Leverage existing knowledge of relationships

Causal Inference:

• Causal inference in the context of causal structural knowledge
• Evaluation of interventional simulation and optimization approaches
• Active learning of a functional system based on causal structural model

Hard- and Software Acceleration:

• Enineering of a modular IT system that enables the application of machine learning techniques in the context of causal inference
• Application of Graphics Processing Units (GPU) to develop efficient implementations
• Examination of integration into In-Memory Databases

Use Cases

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 structural causal graphs in complex production processes, e.g., Bachelor Project: Application of Causal Inference in Automotive Production

Medicine:

• Application of causal inference in the context of gene expression data
• Incorporation of genetic variants and gene expression data to detect causal relationships

Database Operations:

• Examination of causal inference on the basis of system time series data
• Derivation of insights in the context of an unexpected system behavior

Teaching

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

Contact

Johannes Huegle, Christopher Schmidt, Dr. Matthias Uflacker