Advanced Topics
Causal Inference — Beyond Correlation to Causation
Master causal inference methods to move beyond correlation and understand true cause-and-effect relationships in data. Essential for A/B testing and policy evaluation.
- Do-Calculus — Pearl's framework for causal reasoning
- Instrumental Variables — Addressing endogeneity in observational data
- Difference-in-Differences — Estimating causal effects from natural experiments
"Correlation does not imply causation, but it sure does hint."
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 vs Causation Diagram
DAGs (Directed Acyclic Graphs)
Causal DAG Examples
Methods
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)
Potential Outcomes Framework
Key Takeaways
What to Learn Next
-> ML Ethics — Fairness, Bias, Interpretability and Responsible AI Learn about ml ethics — fairness, bias, interpretability and responsible ai.
-> A/B Testing for ML — Experiment Design and Statistical Rigor Learn about a/b testing for ml — experiment design and statistical rigor.
-> Model Evaluation — Metrics, Cross-Validation and Selection Learn about model evaluation — metrics, cross-validation and selection.
-> ML Research Methods — Reading Papers and Reproducibility Learn about ml research methods — reading papers and reproducibility.
-> ML System Design — Architecture and Production Patterns Learn about ml system design — architecture and production patterns.
-> Model Interpretability — SHAP, LIME and Explainable AI Learn about model interpretability — shap, lime and explainable ai.