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Sit down in a coffee bar and count how many of the visitors are wearing glasses and how many have grey hair. You will likely find that amongst all visitors with glasses an increased fraction also has grey hair. There is a correlation between “glasses” and “grey hair” – but wearing glasses does not cause grey hair!

Correlations are ubiquitous but largely meaningless – the interesting element is causation.

If you want to know more about Xplain Data, and about Object Analytics and Causal Inference, please fill out the form below. We will send you our whitepaper (see abstract below). If that sounds relevant to you, we are happy get in contact.

From Correlation to Causation to Artificial Intelligence

Abstract

Correlation does not imply causation. Nevertheless, we are often making decisions based on correlation instead of “cause and effect”. Cause and effect, unfortunately, cannot be proven based on observational data. We may, however, obtain some evidence on causality: Direct causal factors cannot be “explained away”, and an intense search for alternative explanations reveals a small set of direct and potentially causal factors. Holistic data is therefore important for causal inference.
Interventions which aim to drive a system towards a desired goal require knowledge about cause and effect. Causality will therefore be an important pillar for future systems of Artificial Intelligence. Precision medicine and individualized treatments are hardly conceivable without that. We show an example where we predict depressive episodes thereby revealing effects and side-effects of certain drug categories, and how those affect different patient groups

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Learn More about Causal Inference and Object Analytics

Sit down in a coffee bar and count how many of the visitors are wearing glasses and how many have grey hair. You will likely find that amongst all visitors with glasses an increased fraction also has grey hair. There is a correlation between “glasses” and “grey hair” – but wearing glasses does not cause grey hair!

Correlations are ubiquitous but largely meaningless – the interesting element is causation.

If you want to know more about Xplain Data, and about Object Analytics and Causal Inference, please fill out the form below. We will send you our whitepaper (see abstract below). If that sounds relevant to you, we are happy get in contact.

From Correlation to Causation to Artificial Intelligence

Abstract

Correlation does not imply causation. Nevertheless, we are often making decisions based on correlation instead of “cause and effect”. Cause and effect, unfortunately, cannot be proven based on observational data. We may, however, obtain some evidence on causality: Direct causal factors cannot be “explained away”, and an intense search for alternative explanations reveals a small set of direct and potentially causal factors. Holistic data is therefore important for causal inference.
Interventions which aim to drive a system towards a desired goal require knowledge about cause and effect. Causality will therefore be an important pillar for future systems of Artificial Intelligence. Precision medicine and individualized treatments are hardly conceivable without that. We show an example where we predict depressive episodes thereby revealing effects and side-effects of certain drug categories, and how those affect different patient groups

Please fill out this form. We will get back to you as soon as possible with your personal access data.

Your Name (required)

Company (required)

Your Email (required)

Your Message (optional)

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