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A causality engine is a technology platform that learns, understands and draws conclusions based on causation, not merely correlation, of data input.
A causality engine enables business users to:
A causality engine uses a mutual information theory to uncover high-dimensional relationships within data. This approach reveals group effects that otherwise would remain hidden, such as where multiple variables interact and correspond to outcomes in suggestive ways. And, it uncovers important patterns typically overlooked with traditional data science methods.
A causality engine simplifies the process, reduces bias and provides strategic and tactical actions that can be taken in response to change. It evaluates the thousands of possible variables in data—from sales and marketing to human resources, from innovative research and development to learning more from digital twins—and finds relationships. It operates on extremely large datasets to derive valuable knowledge about the combinations of factors that correlate most strongly to specific outcomes.
This “clear-box” approach operates without preconceptions or prewritten models by separating relevant and contributory factors from non-relevant correlative ones to quickly give users insights into which factors predict outcomes. A causality engine adopts the outcome as the precondition for analysis. It then can parse massive amounts of data to identify which variables relate more frequently than others to that outcome.
During this process, it discovers combination effects where factors that are weak predictors individually can be seen as strongly predictive in combination. The system automatically provides multiple recommendations to achieve the targeted goal—a powerful tool for decision-making. Such analysis allows businesses to develop more informed strategies and adopt specific tactics to address causes. Users need only provide their data and their domain-specific goals. An AI causality engine autonomously examines relationships and reports on them, reducing dependence on in- house subject matter experts.
Most machine learning AI platforms base their analytics on known models, developed in multiple iterations by engineers. They develop an algorithm and test a model with a desired outcome in mind. Such iterative model development to refine the AI engine to produce desired outcomes is laborious and costly. It takes time. The machine must be taught how to recognize patterns in data. Moreover, human beings are fallible and have varying ranges of expertise in statistical analysis, data science or particular types of subject matter that are necessary to develop the right types of algorithms to make predictive models work.
Conversely, 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. It builds a unique model for the data it is operating on. The model then refines, trains and corrects itself, yielding factors related more strongly to outcomes, and discovering which variables are the best predictive drivers for the objective.