causal-inference
5 free lessons tagged causal-inference across Business, AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Why Everything Gets Tested, and What a Test Actually Is
The companies famous for experimentation did not adopt it out of statistical enthusiasm: they adopted it because their own data showed most confident product ideas fail to improve the metrics they target. This lesson covers why observational product data misleads, what randomisation actually buys, the choice of randomisation unit, and the humbling base rates reported by the teams who measured.
Causality in Modern Machine Learning
Why prediction systems fail when deployed, how invariance across environments becomes a training signal, and where causal reasoning enters bandits, reinforcement learning, and language models.
Causal Discovery: Learning the Graph
If the graph is an assumption, can you learn it from data instead? Conditional independence testing, the equivalence classes that limit what is knowable, and what changes when unmeasured confounders are allowed.
Identification: When Observational Data Is Enough
The central question of causal inference has a precise answer. The backdoor criterion, the front-door criterion, instrumental variables, and what to do when no identification strategy exists.
Why Correlation Is Not Enough
The formal machinery that makes causal questions answerable: structural causal models, graphs as assumptions you can inspect, the three ways variables become associated, and why prediction and intervention are different problems.