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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

Causal Analysis can be performed in the HPCC Systems platform via the Causality Toolkit bundle.  This toolkit provides a set of leading-edge algorithms for Causal Analysis of big data.  These intensive algorithms are parallelized on HPCC Systems clusters and we are currently utilizing the toolkit to explore ways in which Causal Analysis can help understand and utilize real-world data.

Since Causal Analysis is a fairly new area of scientific inquiry, the algorithms and techniques are rapidly evolving. Various methods have been devised for assessing the correctness of a Causal Model given a dataset thought to be produced by that model.  This project will survey the available methods and assess them to determine the most powerful and practical methods.  The project will result in one or more of the most promising methods being implemented within the HPCC Causality Framework.

The work involves developing test cases and comparing results using various in-house and publicly known validation methods.  The student will design tests, perform tests, and document their results.  Assessment of validation methods will be both qualitative and quantitative, and will include run-time performance as well as accuracy.

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