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