DoOperator Research
All Chapters
10 chapters · read in any order, though 1 → N works best.
- 118 min
Why Experiment?
Most things that look like cause and effect aren't. This chapter explains why — and why randomized experiments are so powerful.
- 220 min
Designing Experiments
How to design an experiment that actually answers your question — power analysis, block designs, compliance, and pre-registration.
- 322 min
Causal Graphs
DAGs (directed acyclic graphs) let you reason visually about causation. Learn the backdoor criterion, what to control for, and the collider trap.
- 420 min
Counterfactuals
The potential outcomes framework, average treatment effects, heterogeneous treatment effects, and how machine learning estimates them.
- 522 min
Bayesian Thinking
Bayes' theorem, priors and posteriors, the Beta-Binomial model, credible intervals, and why Bayesian methods are natural for experiments.
- 625 min
Multi-Armed Bandits
Learn how multi-armed bandits adapt in real time — Thompson sampling, UCB, and why they beat fixed A/B tests for online decisions.
- 728 min
Reinforcement Learning
MDPs, Q-learning, policy gradient methods, and the connection between RL and causal inference for decision-making.
- 824 min
Effect Estimation
OLS, propensity scores, AIPW doubly-robust estimation, and how machine learning improves causal estimation.
- 920 min
When Experiments Fail
How to stress-test causal claims: E-values for unmeasured confounding, Rosenbaum bounds, placebo tests, and observational methods.
- 1026 min
Causal AI
Why ML alone isn't enough for decisions. Causal discovery, invariant risk minimization, counterfactual prediction, and the future of causal AI.