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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. 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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