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

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Stop predicting. Act to change outcomes.

Algorithmic recourse determines the actions required to achieve your desired outcome, while respecting your business constraints. It allows you to generate actionable recommendations to solve your business challenges:

  • The ability to generate accurate and actionable recommendations with recourse is only possible with causality.
  • Embed business constraints directly into the recourse engine to ensure outcomes respect the real world.
  • Act with confidence knowing why actions were suggested with built-in explainability tooling to enhance trusted decision making.
Only Possible With Causality

Cause and effect guides the best course of action.

Algorithmic recourse is only possible with an understanding of cause and effect relationships. This is necessary to quantify how certain actions would influence other variables.

Thanks to algorithmic recourse’s built-in optimizer you don’t need to worry about the complexity of searching to identify the best set of actions, this is done automatically for you.

Embed Business Constraints

Ground actions in the real world.

Configuration of algorithmic recourse to respect the real world constraints which impact your business challenge is seamless:

  • Actionable variables: Which variables can be acted upon in the real world and how they can change. For example the amount of discount a customer receives is actionable, while their age is not.
  • Action cost: Define relative costs of performing actions, to allow the recourse engine to weigh up multiple options.
  • Cost function: Select from pre-populated cost functions, or provide your own, to guide the optimization along the correct path.
Act With Confidence.

Know why actions are recommended.

Use unique built-in explainability tooling to understand both individual and group-level recourse results. Recourse explanations provide:

  • Plain text explanations of why recommendations were selected, allowing non-technical users to gain confidence in actions.
  • How different recommendations compare with one another, and their relative costs.
  • The effect the proposed actions would have on the other variables.