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

Uncertainty-aware personalization with practical decisioning

This notebook is written as a blog-style artifact for practitioners.

From Uplift Modeling to Counterfactual Explanations

This notebook recreates the main ideas and code patterns from Chapter 10 of Causal Inference and Machine Learning by Aleksander Molak, but on a different public dataset.

Towards application of Meta‑Learners(S/T/X)

End-to-End approach for building Causal Models using— DoWhy + EconML + Refutation Tests + GCM API

📘 Which group is more sensitive to treatment? Conditional Average Treatment Error (CATE) & Treatment Heterogeneity

Demonstration with Marketing & Credit data, including comments

Propensity Scores in Practice

A fully commented, marketing-focused notebook

Chapter 4 — Linear Regression for Causal Inference

Bank Marketing Case Study

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