Spring 2020

Epidemiology and Causal Inference

Listed in: Mathematics and Statistics, as STAT-340


Nicholas J. Horton (Section 01)


Epidemiology is the study of the distribution and determinants of disease and health in human populations. It typically involves the analysis of multivariate observational data that pose challenges when trying to make causal conclusions. The course will focus on reasoning about cause and effect, study design, bias and missing data, models and analysis of risk, detection and classification, and modern approaches to confounding and causal inference. Topics include: (1) Measures of disease (incidence and prevalence); (2) Measures of association (relative risk, odds ratio, relative hazard, excess risk, attributable risk); (3) Study designs (exposure and disease base sampling); (4) Assessing significance in a 2x2 table; (5) Assessing significance in a 2x2x2 table; (6) Missing data; (7) Introduction to confounding; (8) Matching; (9) Propensity score adjustment; (10) Unmeasured confounding; (11) Introduction to causal inference and counterfactuals; (12) Causal graphs; and (13) D-separation. Two class meetings per week.

Requisite: STAT-230 (or PSYC 122 and PSYC 200 and consent of the instructor). Spring semester. Professor Horton.


Quantitative Reasoning


2022-23: Not offered
Other years: Offered in Spring 2020