COM SCI M262C · CS M262CCurrent Topics in Causal Modeling, Inference, and Reasoning
Computer Science · 4 units · Graduate courses (200-299)
(Same as Statistics M241.) Review of Bayesian networks, causal Bayesian networks, and structural equations. Learning causal structures from data. Identifying causal effects. Covariate selection and instrumental variables in linear and nonparametric models. Simpson paradox and confounding control. Logic and algorithmization of counterfactuals. Probabilities of counterfactuals. Direct and indirect effects. Probabilities of causation. Identifying causes of events.
Letter grading.
When it runs
Not on the schedule for any of Fall 2025 through Spring 2027. UCLA publishes only that window, so this does not mean the course is gone — check the official listing.
Requisites
Official UCLA wording
Requisite: one graduate probability or statistics course such as course 262A, Statistics 200B, or 202B.
BruinTree reads · Prerequisite
needs reviewconfidence 0.15 · from text- all of
- one of
- one graduate probability
- statistics course such as course 262A
- STATS 200B
- STATS 202B
- · could not read "one graduate probability" (no course number found)
- · could not read "statistics course such as course 262A" (no course number found)
- · mixed comma-level AND/OR in "one graduate probability or statistics course such as course 262A, Statistics 200B, or 202B" — grouping is a best reading
Requires
Everything that has to come before this course, not just the courses named in the requisite above.
COM SCI M262C
- STATS 200BTheoretical Statistics
- STATS 202BMatrix Algebra and Optimization
- STATS 202AStatistics Programming
2 direct requisites. The whole upstream is here — 3 courses over 2 levels. Every course here opens its own tree.
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.