White paper: From correlation to causation to artificial intelligence

White Paper: “From Correlation to Causation to Artificial Intelligence”

Correlation does not equal causation. And unfortunately, cause and effect cannot be proven from observational data – but we can gain important clues about causal relationships. Read how an intensive, algorithmic search for alternative explanations brings to light a small set of direct and potential causal factors. We show an example where we predict depressive episodes, revealing the effects and side effects of specific groups of drugs and how they affect different groups of patients. Thus, causality becomes an important pillar for future AI systems – not only in healthcare.

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White paper: From observational data to causal discovery

White Paper: “From Observational Data to Causal Discovery”

How do you get from “Big Data” to valuable insights? This White Paper explains by way of example the difference between observational data and data collected under experimental conditions – and casts an eye on the challenges posed by purely observational data. Learn what it takes to identify potential cause-and-effect relationships from such data, and what contribution Xplain Data provides to make sense of and intelligently use large volumes of complex observational data – so-called “Big Data”.

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White paper: From relational tables to "object analytics"

White Paper: “From Relational Tables to ObjectAnalytics”

Relational databases are a key element in today’s world of data. They are a perfect solution for what they were built for: transactional management of data, which means that the object (e.g., “The Patient”) is split into different entities and stored in different tables.

This makes it hard to analyze the object “as a whole”. And that’s exactly what ObjectAnalytics facilitates: it is therefore the best solution for “holistic analytics”. This White Paper explains our patented ObjectAnalytics paradigm and the typical analytic operations on whole objects that it supports.

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Vision Paper: “Vision4Health: The Potential of Causal Discovery for the Future of Medicine”

Those who understand how to generate knowledge from data will shape the future of medicine. This vision paper explores the opportunities to accelerate the future of healthcare by aggregating and sharing patient data (real world data) at scale – and extracting knowledge from it by applying our groundbreaking Causal AI methodology (real world evidence). The paper outlines why a 3D view of this data is a prerequisite for gaining insights into causal chains that lead to disease – or recovery – or more.

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