Syllabus

Causal Inference, Surveys, and Missing Data for Population Health

BMI/POP HLTH 661

Fall 2026

Course Information

Course Logistics

Classroom and Meeting Times

Days/Time: Tue/Thu 4:00–5:15 PM
Location: 726 WARF

Instructor

Amy Cochran
Email: cochran4@math.wisc.edu
Office hours: TBD, 685 WARF

Credit Hours

3 credits

UW–Madison credit hour policy. The credit standard for this course is met by an expectation of a total of 135 hours (at least 45 hours per credit) of student engagement with course learning activities. This includes regularly scheduled instructor–student meeting times (75-minute blocks on Tuesday and Thursday), reading, problem sets, and other work described in this syllabus.

Requisites

POP HLTH/B M I 552, F&W ECOL/STAT 572 (or HORT 572 prior to Spring 2025), SOC/C&E SOC 361, or ED PSYCH 761

Course Attributes

Graduate level

Instructional Mode

Face to face

Course Descriptions

Official Description

Overview of modern statistical methods for dealing with “incomplete” data, including the design and analysis of complex surveys, the analysis of missing data, and causal inference.

Instructor Description

This course builds a foundation for making causal claims from observational data. We want to move past associations — this thing is related to that thing — and actually say that one thing causes another. Does vaping give you cancer? Is intermittent fasting bad for your heart? Can I eat eggs every morning? People are acting on these questions regardless (vaping, skipping breakfast, eating eggs), and the science they rely on should meet them with honest answers rather than a shrug about causation.

Making those claims is hard. Without randomization, there are always at least two explanations for any pattern we see, and ruling them out requires both the right tools and honest accounting of their limits. We build those tools from the ground up: causal models and graphs, potential outcomes and causal effects, measured and unmeasured confounding, and sensitivity analysis. We also extend them to practical complications like missing data and complex survey designs.

Those tools are learned by using them. We write code in R, analyze real data in class, and the final project involves emulating a target trial from observational data.

Course Materials

The course draws on several resource texts:

Additional notes and materials will be provided throughout the semester that build on and extend these texts.

Course Format and Expectations

Assessment and Grading

Your course work is weighted out of 100 points:

  • Homework: 15 points
  • Quizzes: 35 points
  • Attendance: 15 points
  • Project: 35 points

Guaranteed Grade Lines

A percentage score in the indicated range guarantees at least the letter grade next to it:

  • A: [93,100]
  • AB: [88,93)
  • B: [83, 88)
  • BC: [78, 83)
  • C: [63, 78)
  • D: [50, 63)
  • F: [0, 50]

Grade lines may be lowered at the end.

Devices

The class will be device-free: the use of laptops, tablets, and phones is prohibited except during class sessions when devices are used for specific learning activities. Students with approved accommodations through the McBurney Disability Resource Center may use devices as specified in their accommodations.

Attendance

Attendance is expected and graded. Full credit is earned with three or fewer unexcused absences. Each additional unexcused absence reduces the attendance grade by an equal share, calculated using the number of scheduled class meetings beyond the three allowed absences.

Some absences can be anticipated and should be arranged with the instructor beforehand: conferences, academic travel, religious observances, or other scheduled commitments. Others arise unexpectedly: illness, family emergencies, or other extenuating circumstances. For these, let the instructor know as soon as you are able. All other absences are unexcused.

Class Meetings

Class meets in person starting promptly at 4:00 PM on Tuesdays and Thursdays. Each class follows the same structure: a short lecture, followed by pen-and-paper problems worked through in small groups, followed by a coding session in R. The instructor will move between groups during problem-solving and coding to ask and answer questions.

This structure only works if you show up ready to engage. That means trying problems before asking for help, supporting peers, and bringing your laptop for the coding portion.

Homework

Homework assignments are due by 4:00 PM on September 17; October 1, 8, 22, and 29; November 5 and 19; and December 8. With the exception of the final assignment, which is due on Tuesday, these dates are Thursdays. Submit each assignment on Canvas as a single PDF before class. There are 8 assignments total.

Homework is graded for completeness, not correctness. Mastery is assessed through quizzes and the project.

  • No late assignments will be accepted.
  • Collaboration on ideas is encouraged, but writing solutions together or copying another person’s work is not allowed.
  • AI tools may be used to brainstorm, explore ideas, or improve writing clarity. You are fully responsible for the accuracy and integrity of what you submit.
  • Neatness and clarity matter. Write one problem per page except for very short problems. Computations without explanation will not receive credit even if the answer is correct.

Quizzes

There are 8 short in-class quizzes, given on the same dates as homework: September 17; October 1, 8, 22, and 29; November 5 and 19; and December 8. The final quiz is on a Tuesday; all other quizzes are on Thursdays. Quizzes assess comprehension of recent material and will look similar to homework problems. The lowest quiz score will be dropped.

Project

The project is the central assessment of the course. Working individually, you will develop a complete protocol for a target trial emulation. The final submission has two parts: a written protocol and an analysis script that executes on a sandbox dataset. The protocol can be written in any format (e.g., Quarto, Word, or LaTeX). The project unfolds across six milestones:

  • Scientific gap: Thursday, September 10
  • Target trial table: Thursday, September 24
  • Construction: Thursday, October 15
  • Identification: Thursday, November 12
  • Estimation: Thursday, December 3
  • Final document: Tuesday, December 15

More detail on each milestone will be provided on Canvas. You are welcome to use your own dataset or work with the dataset the instructor uses throughout the course.

Schedule (Tentative)

Table 2.1: Tentative course schedule for Fall 2026.
Week Date Day Section Topics
1 Sep 3 Th Motivation Cornfield conditions
2 Sep 8 Tu Clinical trials
2 Sep 10 Th Review Probability and statistics review
3 Sep 15 Tu Construction Structural equation models (SEM)
3 Sep 17 Th Nonparametric SEM (NPSEM)
4 Sep 22 Tu Causal graphs
4 Sep 24 Th d-separation
5 Sep 29 Tu Potential outcomes
5 Oct 1 Th SWIGs
6 Oct 6 Tu Causal effects
6 Oct 8 Th Measured confounding Outcome regression
7 Oct 13 Tu Matching
7 Oct 15 Th Propensity score
8 Oct 20 Tu Inverse probability weighting
8 Oct 22 Th Doubly robust methods
9 Oct 27 Tu Flexible modeling methods
9 Oct 29 Th Unmeasured confounding Instrumental variables
10 Nov 3 Tu Regression discontinuity
10 Nov 5 Th Other strategies
11 Nov 10 Tu Sensitivity E-values
11 Nov 12 Th Gamma / Rosenbaum sensitivity
12 Nov 17 Tu Missing data Missing data mechanisms
12 Nov 19 Th Multiple imputation
13 Nov 24 Tu Chained equations
13 Nov 26 Th Thanksgiving (no class)
14 Dec 1 Tu Missing not at random
14 Dec 3 Th Surveys Complex survey design
15 Dec 8 Tu Weighting and variance estimation

Official UW Statements

Accommodations for Students with Disabilities

The University of Wisconsin–Madison supports the right of all enrolled students to a full and equal educational opportunity. The Americans with Disabilities Act (ADA), Wisconsin State Statute (36.12), and UW–Madison policy (UW-855) require the university to provide reasonable accommodations to students with disabilities to access and participate in its academic programs and educational services. Faculty and students share responsibility in the accommodation process. Students are expected to inform faculty of their need for instructional accommodations during the beginning of the semester, or as soon as possible after being approved for accommodations. Faculty will work either directly with the student or in coordination with the McBurney Disability Resource Center to provide reasonable instructional and course-related accommodations. Disability information, including instructional accommodations as part of a student’s educational record, is confidential and protected under FERPA.

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Lecture materials and recordings for this course are protected intellectual property at UW–Madison. Students enrolled in this course may use the materials and recordings for their personal use related to participation in the course. Students may also take notes solely for their personal use. If a lecture is not already recorded, students are not authorized to record lectures without permission unless they are considered by the university to be a qualified student with a disability who has an approved accommodation that includes recording. Students may not copy or have lecture materials and recordings outside of class, including posting on internet sites or selling to commercial entities, with the exception of sharing copies of personal notes as a notetaker through the McBurney Disability Resource Center. Students are otherwise prohibited from providing or selling their personal notes to anyone else or being paid for taking notes by any person or commercial firm without the instructor’s express written permission. Unauthorized use of these copyrighted lecture materials and recordings constitutes copyright infringement and may be addressed under the university’s policies, UWS Chapters 14 and 17, governing student academic and non-academic misconduct. View more information about FERPA.

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AI Statement

The use of artificial intelligence (AI) tools and applications, such as Copilot, Gemini, and others, is permitted in this course when it supports the course learning objectives. You are responsible for any information you submit based on an AI query. Ensure that you have permission before posting course content, including assignment or assessment prompts, and verify that AI-generated results do not contain misinformation or unethical content. Your use of AI tools must be documented and cited to conform to this course’s expectations. When you use AI on a submitted assignment, include a brief AI-use statement naming the tool and describing how you used it. You remain responsible for the correctness, clarity, and completeness of all submitted work.

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