Decision-Making Under Uncertainty
A practical guide to experiments, causality, Bayesian thinking, and machine learning — written for curious people, not just academics.
Why Randomize?
Understand why randomized experiments are the gold standard for causal claims — and when they're not enough.
Causal Graphs
Learn to draw and reason with DAGs: spot confounders, find adjustment sets, and avoid classic traps like collider bias.
Bandits & RL
Go beyond A/B tests: learn how multi-armed bandits balance exploration and exploitation in real time.
Causal AI
See how machine learning and causal inference combine — from doubly-robust estimators to counterfactual prediction.
Table of Contents
All 10 Chapters
Read in any order, though 1 → N works best.
- 118 min
Why Experiment?
The surprisingly tricky problem of figuring out what actually causes what
- 220 min
Designing Experiments
Sample sizes, randomization schemes, and how not to fool yourself
- 322 min
Causal Graphs
Drawing the invisible: how to see confounders, mediators, and colliders
- 420 min
Counterfactuals
What would have happened? The language of causation
- 522 min
Bayesian Thinking
Updating beliefs with evidence, from Bayes' theorem to posterior distributions
- 625 min
Multi-Armed Bandits
The exploration-exploitation trade-off and why A/B tests waste half your traffic
- 728 min
Reinforcement Learning
Sequential decision-making: when actions have long-term consequences
- 824 min
Effect Estimation
Getting the numbers right: regression, matching, and doubly-robust methods
- 920 min
When Experiments Fail
Sensitivity analysis, E-values, and what to do when you can't randomize
- 1026 min
Causal AI
Combining machine learning with causal reasoning