2 citations · 2 across the 4 of their papers we have counts for
4 papers
Identifying counterfactual probabilities using bivariate distributions and uplift modeling
Théo Verhelst, Gianluca Bontempi
Uplift modeling estimates the causal effect of an intervention as the difference between potential outcomes under treatment and control, whereas counterfactual identification aims…
Ethics2vec: aligning automatic agents and human preferences
Gianluca Bontempi
Though intelligent agents are supposed to improve human experience (or make it more efficient), it is hard from a human perspective to grasp the ethical values which are explicitly…
A churn prediction dataset from the telecom sector: a new benchmark for uplift modeling
Théo Verhelst, Denis Mercier, Jeevan Shrestha +1
Uplift modeling, also known as individual treatment effect (ITE) estimation, is an important approach for data-driven decision making that aims to identify the causal impact of an…
Between accurate prediction and poor decision making: the AI/ML gap
Gianluca Bontempi
Intelligent agents rely on AI/ML functionalities to predict the consequence of possible actions and optimise the policy. However, the effort of the research community in addressing…