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AI & MLSpecializedAIML-CAU-008

Causal Discovery and Structure Learning

Recovering a causal graph from data: independence tests, Markov equivalence, and what data cannot orient.

Difficulty
5 / 5
Importance
3 / 5
Study time
1–2 h
Kind of work
Mixed

Why it matters

The learner can run the PC algorithm on a small set of conditional-independence results, recover the Markov equivalence class, and name the assumptions (faithfulness, no hidden confounders) that the recovered graph depends on.

The route to this topic

Route AIML-CAU-008
Natural Numbers What Is a Model? Variables & Constants Learning from Data Features and Targets Solving Linear Equations Function Notation & Composition Correlation vs Causation Linear Regression I: Prediction and Residuals Complex Numbers Prediction versus Causation Loss and Fitting: Squared Error Basic Ratios Unit Circle Objective Functions and Empirical Risk Polynomial Functions Likelihood Intuitive Limits Continuity Definition of the Derivative Differentiation Rules Bayesian Reasoning Chain Rule Antiderivatives & Indefinite Integrals Derivatives of Transcendental Functions Riemann Sums & Definite Integral Fundamental Theorem of Calculus Integration by Parts Sample Spaces & Events Conditional Probability & Independence Bayesian Networks Conditional Independence and d-Separation Causal Graphs and the Backdoor Criterion Causal Discovery and Structure Learning