Skip to Content

The Causal AI conference is back in San Francisco for 2024, bigger and better than ever.

Register Interest

White papers

Read about the latest use cases and real-world applications of Causal AI

AI that speaks the Language of Executives and is aligned to their Objectives

Traditional AI techniques & Generative AI are applicable for certain use cases, especially around unstructured data, but do not offer promise for improving decision making in the enterprise

Read more

The Causal AI Revolution is Underway

Enterprises such as Microsoft and Amazon are adopting Causal AI to revolutionise AI decision making. Here's why you should consider it too.

Read more

How Can AI Discover Cause and Effect?

Causal AI autonomously finds causes, using “causal discovery algorithms”, while boosting experimentation and human intuition.

Read more

Optimizing Pricing & Promotion Powered by Causal AI

Investments, strategies, and experiments are powerful tools for increasing revenue, margin, and category leadership.

Read more

I Need Causal AI- Now What? Build vs Buy

Should you develop Causal AI capabilities in-house, or buy the technology off-the-shelf? We think “build versus buy” is a false dilemma.

Read more

Why Causal AI Models Outrun Bayesian Networks

Both types of models have some similarities, but they also have significant differences. BNs simply describe patterns. Causal AI models capture the underlying processes that drive those statistical relationships.

Read more

Next-Best-Action with Causal AI

Suppose that your marketing team has identified that one of your business’s key demographics (18-25 year-olds) are at risk of “churning” (abandoning the service).

Read more

Can AI be Fair?

Machine learning systems amplify human biases. Find out how Causal AI de-biases algorithms and promotes fairness.

Read more

The Real AI Revolution: Machines That Learn Like Scientists

An understanding of causality takes machines beyond learning towards having abilities that mean they might reasonably be described as machine scientists.

Read more
image description