STATS 420 · STAT 420Causal 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
- Fall 2026
Scheduled, not typical — from UCLA’s Schedule of Classes, which publishes Fall 2025 through Spring 2027 and nothing before it.
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.
- · could not read "familiarity with basic probability theory" (no course number found)
- · could not read "regression modeling" (no course number found)
- · could not read "statistical computing environment (R or Python)" (no course number found)
- · no course reference could be read from this requisite
Requires
Everything that has to come before this course, not just the courses named in the requisite above.
Nothing — this is an entry point.
Unlocks
What this course is a requisite for, and what those courses lead to in turn.
No course in the catalog lists this as a requisite.