STATS 420
Causal Inference
Statistics · 4 units · Graduate professional courses (400-499)
Limited to Master of Applied Statistics students. Offers principled foundation for causal reasoning using Pearl’s causal hierarchy (PCH) and structural causal models (SCMs), and develops practical tools for answering causal questions from real-world data. Students learn to distinguish observational, interventional, and counterfactual reasoning; to design and analyze experiments; and to draw valid causal conclusions when experimentation is not possible. Also covers modern topics at intersection of causal inference and artificial intelligence. Applications are drawn from technology, business, and health sciences.
Letter grading.
When it runs
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Requisites
Official UCLA wording
Recommended preparation: familiarity with basic probability theory, regression modeling, and statistical computing environment (R or Python).
BruinTree reads · Recommended
needs reviewconfidence 0.00 · from textBruinTree could not read this requirement — see UCLA’s wording above.
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Requires
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Unlocks
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