Causal Discovery Algorithms

This project is available as a student work experience opportunity with HPCC Systems. Curious about other projects we are offering? Take a look at our Ideas List.

Student work experience opportunities also exist for students who want to suggest their own project idea. Project suggestions must be relevant to HPCC Systems and of benefit to our open source community. 

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Project Description

A wide variety of causal discovery algorithms have been described and implemented to date.  This project will evaluate the available algorithms against mixed-data-type, real-world datasets using open-source implementations.  Algorithms will be evaluated for power, practicality, and applicability to different data-types. 

The work involves identifying candidate datasets, define appropriate analytics, perform causal analysis and publish results.  The student will design tests, perform tests, and document their results comparing various in-house and publicly available algorithms.  Assessment of algorithms will be both qualitative and quantitative, and will include run-time performance as well as accuracy.

The successful candidate should have a background in mathematics and statistics, machine learning, and preferably knowledge of Causal Science, Causal algorithms and Causal analysis packages. 

If you are interested in this project, please contact the mentor shown below.

More information about the HPCC Systems Causality Toolkit is available in our blog Causality 2021.