Causal Inference — Complete Guide

Causal inference goes beyond correlation to answer "what if" questions. Essential for treatment effects and decision-making.

Correlation vs Causation

Correlation: X and Y occur together
Causation: X causes Y

Example:
Ice cream sales ↔ Drowning deaths
Correlation: Yes
Causation: No (both caused by hot weather)

Causal inference asks: "What would happen if we TREAT?"
Not: "What happens when we OBSERVE?"

Methods

Randomized Control Trial (RCT):
├─ Gold standard
├─ Random assignment eliminates confounders
└─ Simple, unbiased

Observational Methods:
├─ Propensity Score Matching
├─ Instrumental Variables
├─ Difference-in-Differences
├─ Regression Discontinuity
└─ Double Machine Learning

Uplift Modeling:
├─ Predict treatment effect, not outcome
├─ Causal Forest
└─ Meta-learners (T-learner, S-learner, X-learner)

Key Takeaways

1. Correlation ≠ Causation — always
2. RCTs are the gold standard for causal inference
3. Observational methods when RCTs aren't possible
4. Uplift modeling predicts treatment effects
5. Confounding is the main challenge
6. Counterfactuals define causal effects
7. Causal inference requires domain knowledge
8. ML + causal inference enables better decisions