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AI & MLRecommendedAIML-CAU-003

Causal Graphs and the Backdoor Criterion

Encoding causal assumptions as a DAG and deciding which variables to adjust for.

Difficulty
3 / 5
Importance
4 / 5
Study time
1–2 h
Kind of work
Mixed

Why it matters

The learner can identify a valid backdoor adjustment set in a causal DAG and name a variable whose adjustment introduces bias.

The route to this topic

Route AIML-CAU-003
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 Causal Graphs and the Backdoor Criterion