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This project is already taken and is no longer available for the 2023 HPCC Systems Intern Program

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. 

Find out about the HPCC Systems Summer Internship Program.

Project Description

Nature has left us with some very subtle signals within data to indicate the causal relationships that generated that data.  These signals can be hard to detect, prone to statistical error, and while we do have mechanisms to construct causal models from data, at this point, they tend to be brittle, expensive, and unreliable. HPCC Systems can help overcome the challenge of identifying causal signals from data by allowing This makes the HPCC Systems Platform a natural environment for doing Causality research and application, since far more data to can be processed efficiently via complex and causal discovery algorithms can parallelize nicely, leading to much faster causal analysis results.

A wide variety of causal discovery algorithms have been described and implemented to date but remain to be thoroughly evaluated in HPCC Systems.  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. 

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