Neural-PathPart V
Measurement
Rigorous experimentation and causal thinking. Hypothesis testing, A/B testing design, and causal inference — how to make confident decisions when the data is noisy.
4 modules18 lessons
23
Experimentation
The statistics and design of A/B tests: hypothesis testing, variance reduction, and validity threats.
4 lessonsStart →
24
Causal Inference
When experiments fail: potential outcomes, DAGs, DiD, RDD, IV, and modern observational methods.
4 lessonsStart →
25
Advanced Experiment Design
Geo holdouts, switchback designs, network experiments, always-valid inference, and long-run measurement — for when standard A/B testing breaks down.
5 lessonsStart →
26
Modern Causal Methods
Synthetic control, staggered DiD, Double ML, causal forests, and sensitivity analysis — the state-of-the-art toolkit for rigorous causal estimation at scale.
5 lessonsStart →