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Causality Engine

What is a causality engine?

A causality engine is a technology platform that learns, understands and draws conclusions based on causation, not merely correlation, of data input. While most automated machine learning (autoML) platforms develop an algorithm and test a model with a desired outcome in mind, a causality engine bypasses preconceptions and predetermined algorithms. It first adopts a hypothesis as an outcome, then parses massive amounts of data to determine which factors align most closely with that result.

What are the business benefits of a causality engine?

A causality engine enables business users to:

  • Better understand and address the bias and predictive signals hidden in data.
  • Gain the correct actionable insights and models to explain predictions and ensure the quality level of predictive behaviors in data.
  • Quickly determine what matters most in a data set and pinpoint the best actions to take in order to achieve the desired business outcomes.
  • Prioritize causal and relevant factors, and dispense with non-relevant correlative ones, to know what drives certain results and select an effective course of action to achieve them.
  • Generate outcomes even in volatile business environments, ignoring outlier or missing data and quickly amassing and adjusting to new data.

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