From Models to Decisions

Algorithmic Recourse

Suppose you have constructed a causal model that is able to quantify how exercise, medication and stress management tools affect blood pressure - the natural question is: how to use this information to get the optimal intervention, for each individual? Each intervention (for instance, taking a daily medication) will carry its costs and risks - as well as an expected effect on blood pressure. Furthermore, combining different interventions can also have a nonlinear effect on the outcome. 

Algorithmic recourse is a decision intelligence engine that allows us to, given a causal model, answer the above question in a seamless way. Effectively, it’s giving is “the set of interventions with the minimal cost that is able to achieve a certain goal”. This is a tool that can be used in any setting where there’s a decision boundary to be crossed: for blood pressure, this can be phrased as reaching 120/80 - for customer retention, it could be ensuring with 50% confidence that a customer will not churn.