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Causal Inference

Moving beyond correlation to cause — the tools for asking 'what if?' and 'why?', not just 'what is likely?'.

Causal Inference is one of the core areas in the AI University map of AI. Explore the diagram, then dive into each topic — every subtopic grows into its own deep-dive over time.

flowchart LR
  OBS[/Observed data/] --> ASSUME[Causal graph<br/>assumptions] --> ID{Identifiable?}
  ID -->|yes| EST[Estimate effect] --> ACT[/Decision/]
  ID -->|no| EXP[Run experiment] --> EST

Key topics

  • Correlation vs causation

    Why predictive accuracy alone isn't enough for decisions and interventions.

  • Causal graphs & do-calculus

    Representing assumptions as DAGs and reasoning about interventions.

  • Counterfactuals

    Estimating what would have happened under a different action.

  • Experiments & A/B testing

    Randomized trials — the gold standard for causal claims.

  • Observational methods

    Instrumental variables, matching, and difference-in-differences when you can't experiment.

  • Uplift modeling

    Predicting the effect of an action per individual — who to treat, not just who will convert.

Machine Learning · Data & MLOps · Recommender Systems

Learn this properly

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