From uplift ranking to uncertainty-aware decisions on the Criteo dataset

Uncertainty-aware personalization with practical decisioning

From Uplift Modeling to Counterfactual Explanations

From Estimation to Practical Application

From Assumptions to Counterfactuals: A Practical Causal Modeling Workflow

Evaluating uplift models using ranking-based metrics across real datasets

Abstract

Correcting Selection Bias with IPW and Doubly Robust Estimation

Abstract

Estimating Treatment Effects with Linear Regression

Abstract

Causal Graphs, Confounding, Colliders, and Selection Bias

A Practical Tutorial Inspired by Chapter 3 (with Original Examples)

Randomized Experiments and Stats Review

A Minimal Demo in Python