2 citations · 4 across the 5 of their papers we have counts for
5 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…
Probability of collision in nonlinear dynamics by moment propagation
Théo Verhelst, Giacomo Acciarini, Dario Izzo +1
Estimating the probability of collision between spacecraft is crucial for risk management and collision-avoidance strategies. Current methods often rely on Gaussian assumptions and…
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…
Uplift vs. predictive modeling: a theoretical analysis
Théo Verhelst, Robin Petit, Wouter Verbeke +1
Despite the growing popularity of machine-learning techniques in decision-making, the added value of causal-oriented strategies with respect to pure machine-learning approaches has…
Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment
Théo Verhelst, Denis Mercier, Jeevan Shrestha +1
Counterfactuals are central in causal human reasoning and the scientific discovery process. The uplift, also called conditional average treatment effect, measures the causal effect…