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STATS C160 · STAT C160Causal Inference for Health Data

Statistics · 4 units · Undergraduate upper division (100-199)

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Recommended: course 102A is recommended but not required, especially for students with high proficiency in python. Covers the foundations of the graphical approach to causal inference, with a specific focus on applications to health data, analyzing a range of real-world datasets. Concurrently scheduled with course C260.

P/NP or 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

Requisites: course 20 or 21 or Program in Computing 16A, and courses 100A, 101A. Preparation: basics of probability theory and regression modelling.

BruinTree reads · Prerequisite

confidence 1.00 · from UCLA’s structured data
all of
  • STATS 100A
  • STATS 101A
  • one of
    • STATS 20
    • STATS 21
    • COMPTNG 16A

BruinTree reads · Preparation

needs reviewconfidence 0.00 · from text

BruinTree could not read this requirement — see UCLA’s wording above.

  • · could not read "basics of probability theory" (no course number found)
  • · could not read "regression modelling" (no course number found)
  • · no course reference could be read from this requisite

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About STATS C160. We read UCLA’s requisite wording by machine, and it gets things wrong.

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Requires

Everything that has to come before this course, not just the courses named in the requisite above.

STATS C160

  • STATS 20Introduction to Statistical Programming with R
    • STATS 10Introduction to Statistical Reasoning
    • STATS 12Introduction to Statistical Methods for Geography and Environmental Studies
    • ECON 41Probability and Statistics for Economists
      • MATH 31ADifferential and Integral Calculus1 more beneath
      • MATH 31BIntegration and Infinite Seriesanother path to it
    • STATS 13Introduction to Statistical Methods for Life and Health Sciences
    • STATS 15Introduction to Data Scienceanother path to it
    • PSYCH 100APsychological Statisticsanother path to it
  • STATS 21Python and Other Technologies for Data Science
    • STATS 20Introduction to Statistical Programming with Ranother path to it
  • COMPTNG 16APython with Applications I
    • COM SCI 31Introduction to Computer Science I
    • COMPTNG 10AIntroduction to Programming
  • STATS 100AIntroduction to Probability
    • MATH 32BCalculus of Several Variables
      • MATH 31BIntegration and Infinite Series
      • MATH 32ACalculus of Several Variables
    • MATH 33ALinear Algebra and Applications
      • MATH 3BCalculus for Life Sciences Students1 more beneath
      • MATH 31BIntegration and Infinite Seriesanother path to it
      • MATH 32ACalculus of Several Variablesanother path to it
  • STATS 101AIntroduction to Data Analysis and Regression
    • STATS 10Introduction to Statistical Reasoninganother path to it
    • STATS 12Introduction to Statistical Methods for Geography and Environmental Studiesanother path to it
    • ECON 41Probability and Statistics for Economistsanother path to it
    • STATS 15Introduction to Data Science
    • PSYCH 100APsychological Statistics
      • STATS 10Introduction to Statistical Reasoninganother path to it
      • COMPTNG 10AIntroduction to Programminganother path to it
      • PSYCH 10Introductory Psychology

5 direct requisites. Showing 31 courses over 3 levels; the branches marked with a count carry on past it. 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.