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1. Data, statistical models, and ‘what is causality’

2. Biases, causal frameworks, and causal estimands

3. Planning and designing credible causal analyses

4. Experiments I: designing and running reliable experiments

5. Experiments II: Sample size, power, and detecting real effects

6. Causal assumptions: think first, regress later

7. Instrumental variables: when people don’t do what they were assigned to

8. Regression discontinuity design: cutoffs as natural experiments

9. Two-way fixed effect: the old difference-in-differences

10.  Staggered treatment: the new difference‑in‑differences

11. Time series methods: measuring impact without a control group

12. Heterogeneous treatment effects: different people, different reactions

13. Translating causal estimates into metrics of business value

14. Falsification: stress test your results before someone else does

15. Mechanisms: opening the black-box-parts of causality

16. Alternative causal philosophies and frameworks