STATS M241
Current Topics in Causal Modeling, Inference, and Reasoning
Statistics · 4 units · Graduate courses (200-299)
(Same as Computer Science M262C.) 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.
Requisites
Official UCLA wording
Requisite: one graduate probability or statistics course such as course 200B, 202B, or Computer Science 262A.
BruinTree reads · Prerequisite
needs reviewconfidence 0.00 · from text- all of
- one of
- one graduate probability
- statistics course such as course 200B
- 202B
- COM SCI 262A
- · could not read "one graduate probability" (no course number found)
- · could not read "statistics course such as course 200B" (no course number found)
- · could not read "202B" (department of the preceding reference is unknown)
- · mixed comma-level AND/OR in "one graduate probability or statistics course such as course 200B, 202B, or Computer Science 262A" — grouping is a best reading
Requires
Everything that has to come before this course, not just the courses named in the requisite above.
STATS M241
- COM SCI 262ALearning and Reasoning with Bayesian Networks
- EC ENGR 131AProbability and Statistics
- COM SCI 112Modeling Uncertainty in Information Systems
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.