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Automatically transform raw data into sophisticated Causal AI models that deliver not only superior predictions but are also intrinsically explainable & augment the decision making process factoring real-world constraints

What are Causal AI Models?

Causal AI models represent how systems work, while today’s ML models just track patterns in data. Causal AI models allow users to evaluate the impact of actions ahead of time and answer “why”-questions, going far beyond standard predictive analytics. And Causal AI models are intrinsically explainable, whereas conventional AI is a black box technology. causaLab makes Causal AI models accessible to everyone.

Causal Graph Discovery

Conventional ML just identifies correlations — but correlation is not causation

  • Prevent simplistic decision-making and model fragility due to misleading correlations.
  • Access the best of academic & proprietary research for causal graph discovery.
  • Incorporate human guidance, domain expertise and background context into causal graph building.

Causal AI Model Building

From a causal graph to a predictive model 

  • Transform causal graphs into predictive models with our proprietary architectures.
  • Access a library of popular machine learning algorithms adjusted to respect cause-and-effect relationships.
  • Constrain the behaviour of models, ensuring that they bake in reasonable assumptions, behave reliably in unusual situations and are always compliant.

See causaLab in action for your organization

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