Why Causal AI

Current state-of-the-art machine learning has severe limitations in dynamic environments and fails to unlock the true potential of AI for businesses.

Causal AI is a new category of intelligent machines that understand cause and effect ― a major step towards true AI.

State-of-the-art ML versus Causal AI

Traditional Machine Learning

  • Static models

  • Historic correlations

  • Black box

  • Observational data only

  • Predictions only

Current ML technology fails when applied to dynamic, complex systems. It produces static models that overfit to yesterday’s world. The models are data-hungry and unintelligible to humans.

causaLens has the solution. Models built with causal AI cut through the noise to identify enduring causal signals. They rapidly adjust to changing conditions, maintaining performance even as the world changes. And they’re fully transparent to humans.

Climbing the ladder of causality

Intuitively incorporate domain knowledge and expertise into the modelling process.

Gain insight into the effects of actions and design optimal interventions.

Imagine counterfactual worlds to explain the past and navigate an uncertain future.

Causal AI

Cutting-edge expertise

We believe that teaching machines to understand cause and effect will unlock the true power that AI has to offer, transforming business efficiency and providing unimaginable benefits to the world.

This is a formidable task. As such, we are committed to building the world’s largest Causal AI research lab to succeed in this effort.

Download our latest research paper Join Us

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