Project Description
Causal inference methods are a set of tools for analyzing and estimating cause-and-effect relationships in data. They include techniques for identifying causal structures, deciding whether causal effects can be estimated from observational data, and computing the outcomes of interventions. These methods are used in various fields such as medicine, economics, social sciences, and machine learning to support decision-making. For example, they can be used to estimate how much a medical treatment reduces disease risk, whether an economic policy increases employment, or how changing a feature in a machine learning model affects predictions.
Our group developed and maintains two widely used open-source software packages for performing causal analysis:
The goal of this project is to extend the functionality of one or both of these tools by integrating modern causal inference methods. This may include causal discovery algorithms, identification techniques, causal validation tools, or simulation capabilities. The project offers practical experience in open-source development, scientific programming, and working at the intersection of theory and real-world application. Depending on your interests, the work can focus on algorithm implementations, performance improvements, or enhancing usability. The project can also be tailored into a research internship or a master’s thesis.
People
Ankur Ankan, Johannes Textor
Requirements
Overview