Course Description:
This graduate-level course offers a practical introduction to causal inference and Bayesian statistics, designed specifically for HCI researchers who want to move beyond correlational analysis and make confident causal claims. Whether you design experiments or work with observational data, this class will transform how you think about data analysis—from an afterthought to a principled framework that shapes your research from the start. You'll learn to identify causal effects using Pearl's structural causal models (DAGs, do-calculus, backdoor criterion), Rubin's potential outcomes framework (propensity score matching, regression discontinuities), and Bayesian statistical methods including Markov Chain Monte Carlo. The emphasis is on hands-on learning: you'll code these concepts in Python and apply them to a final project of your choice, gaining the skills to confidently discuss causal reasoning with your advisor and peers.
This is not a traditional statistics class filled with inscrutable tests and p-values. Instead, we take a Bayesian approach that aligns with how we naturally think—updating our beliefs as we see new data and focusing on effect sizes rather than binary "did it work" questions. You'll learn to avoid common pitfalls like conditioning on colliders, regressing on the wrong variables, or leaving out crucial confounders that can obscure or create false causal effects. The course follows Richard McElreath's "Statistical Rethinking" with additional insights from Pearl's "Causality" and Imbens and Rubin's "Causal Inference in Statistics, Social, and Biomedical Sciences." By the end, you'll be equipped to design better studies, analyze your data transparently, and make rigorous causal claims that stand up to scrutiny.
Spring 2026 Meeting Times and Location: Wednesdays and Fridays 12:30pm--1:45pm. 4th floor, classroom A (Chicago), 0220 Siebel Center for Computer Science (Urbana); Zoom link links on Canvas
Prof. Hari Sundaram, hs1@illinois.edu, Office hours: Wednesdays 10am-11am, Online
Spring 2026 TA: Yuen Chen, yuenc2@illinois.edu, Office hours: TBA
You can find the join codes for Campuswire and Gradescope on Canvas.
This class is taught across two locations: Urbana and Chicago. I will be teaching from Chicago most Wednesdays, and Fridays and the class will be connected via Zoom to Urbana..
This class focuses on students learning by doing — students will learn key ideas by working on open ended assignments, and projects. Note: late assignments are not accepted.; see the late submissions policy below.
Students have the opportunity to dive deeper into the topics discussed in class through research projects on a topic related to the class. For the research project the expectation is that the research should be of publishable quality at a top tier conference. Examples of projects reports will be shown here soon.
Use of generative AI technologies is allowed and encouraged in this class, provided you engage with them critically and transparently. Large language models can serve as valuable thinking partners—helping you explore ideas, debug code, understand complex concepts, or refine your writing—but they cannot replace your own deep engagement with the material. When you use AI tools for course assessments (homework, projects), you must demonstrate that you understand the underlying concepts and can critically evaluate AI-generated content.
Your Obligation: clearly documenting when and how you used AI, verifying any claims or code the AI produces, and being able to explain your reasoning independent of the tool. Think of AI as a collaborator that helps you think more deeply, not a shortcut that thinks for you. Your submissions should reflect your own critical analysis, original insights, and genuine understanding of causal inference concepts. Tools for grammar, spell-checking, and rephrasing remain welcome as before. The goal is to develop your ability to think causally and statistically—skills that require active reflection, not passive consumption of AI outputs.
| Type | Number | Points | Location |
|---|---|---|---|
| Long Homework | 2 | 50 (25 each) | Gradescope |
| Final Project | 1 | 45 | Gradescope |
| Class Participation | - | 5 | In Class |
| Total | 100 |
The grade cutoffs are (lower end of the range is shown for each grade; the upper bound is below the next higher grade):
A+: ≥ 98, A ≥ 90, A- ≥ 85, B+ ≥ 80, B ≥ 75, B- ≥ 70, C+ ≥ 65, C ≥ 60, C- ≥ 55, D ≥ 45, E ≥ 35, F ≤ 35
The scores will be rounded upwards and then the grade will be assigned. So for example, a score of 89.4 would be rounded to 90 and receive an A.
| Lecture Location | Class Date | Topic | Assessments |
|---|---|---|---|
| Chicago | Week 1: Jan. 21, 23 | Bayesian Inference (The Golem's Logic) | |
| Chicago | Week 2: Jan. 28, 30 | MCMC & Generalized Linear Models (Powering Up the Golem) | |
| Chicago | Week 3: Feb. 4, 6 | Linear Models & Causal Inference (Building Golems, Introducing DAGs); Causes, Confounds & Colliders (key ideas) | |
| Chicago | Week 4: Feb. 11, 13 | Instrumental variables and counterfactuals (advanced DAGs) | |
| Chicago | Week 5: Feb. 18, 20 | Overfitting and interactions (learning to critique) | L-HW #1 Released (Fri, Feb. 20, 5pm) |
| Chicago | Week 6: Feb. 25, 27 | Integers & Other Monsters (Specialized Golems) | Project Proposal Due (Mon, Mar. 2, 5pm) |
| Chicago | Week 7: Mar. 4, 6 | Multilevel Models 1 (Models within Models) | L-HW #1 Due (Fri, Mar. 6, 5pm) |
| Chicago | Week 8: Mar. 11, 13 | Multilevel Models 2 | |
| Mar. 14–22, 2026 | Spring Break | ||
| Chicago | Week 9: Mar. 25, 27 | Measurement & Missingness (Handling the Large World) | L-HW #2 Released (Fri, Mar. 27, 5pm) |
| Chicago | Week 10: Apr. 1, 3 | Introduction to Potential outcomes and assignment mechanisms | Mid-Semester Report Due (Mon, Apr. 6, 5pm) |
| Chicago | Week 11: Apr. 8, 10 | Classical Randomization: Fisher P-value, Neyman's sampling approach, Stratification | L-HW #2 Due (Fri, Apr. 10, 5pm) |
| Chicago | Week 12: Apr. 15, 17 | Regular Assignment Mechanism Design (Unconfoundedness, propensity scores) | |
| Chicago | Week 13: Apr. 22, 24 | Dealing with Covariates (overlap, balance, trimming) | |
| Chicago | Week 14: Apr. 29, May 1 | New techniques (synthetic controls, regression discontinuity designs) | |
| Chicago (Last Day!) | Week 15: May 6 | Recap | Final Project Report Due (Thu, May 7, 5pm) |
All assignments are due at the date and time marked for the Assignments. Late submissions will not be accepted except in the case of documented emergencies or religious absences approved in advance. If you have any questions about the policies, please ask the instructor.
The University of Illinois at Urbana-Champaign Student Code should also be considered as a part of this syllabus. Students should pay particular attention to Article 1, Part 4: Academic Integrity. Read the code here. Also, read the CS honor code here.
Academic dishonesty may result in a failing grade. Every student is expected to review and abide by the Academic Integrity Policy. Ignorance is not an excuse for any academic dishonesty. It is your responsibility to read this policy to avoid any misunderstanding. Do not hesitate to ask the instructor(s) if you are ever in doubt about what constitutes plagiarism, cheating, or any other breach of academic integrity.
Illinois law requires the University to reasonably accommodate its students' religious beliefs, observances, and practices in regard to admissions, class attendance, and the scheduling of examinations and work requirements. You should examine this syllabus at the beginning of the semester for potential conflicts between course deadlines and any of your religious observances. If a conflict exists, you should notify your instructor of the conflict and request appropriate accommodations. This should be done in the first two weeks of classes.
All members of the Illinois Computer Science department - faculty, staff, and students - are expected to adhere to the CS Values and Code of Conduct. The CS CARES Committee is available to serve as a resource to help people who are concerned about or experience a potential violation of the Code. If you experience such issues, please contact the CS CARES Committee. The instructors of this course are also available for issues related to this class.
To obtain disability-related academic adjustments and/or auxiliary aids, students with disabilities must contact the course instructor and the Disability Resources and Educational Services (DRES) as soon as possible. To contact DRES, you may visit 1207 S. Oak St., Champaign, call 333-4603, email or go to this URL. If you are concerned you have a disability-related condition that is impacting your academic progress, there are academic screening appointments available that can help diagnosis a previously undiagnosed disability. You may access these by visiting the DRES website and selecting “Request an Academic Screening” at the bottom of the page.
Diminished mental health, including significant stress, mood changes, excessive worry, substance/alcohol abuse, or problems with eating and/or sleeping can interfere with optimal academic performance, social development, and emotional wellbeing. The University of Illinois offers a variety of confidential services including individual and group counseling, crisis intervention, psychiatric services, and specialized screenings at no additional cost. If you or someone you know experiences any of the above mental health concerns, it is strongly encouraged to contact or visit any of the University’s resources provided below. Getting help is a smart and courageous thing to do -- for yourself and for those who care about you.
Counseling Center: 217-333-3704, 610 East John Street Champaign, IL 61820
McKinley Health Center: 217-333-2700, 1109 South Lincoln Avenue Urbana, IL 61801
The University of Illinois is committed to combating sexual misconduct. Faculty and staff members are required to report any instances of sexual misconduct to the University’s Title IX Office. In turn, an individual with the Title IX Office will provide information about rights and options, including accommodations, support services, the campus disciplinary process, and law enforcement options.
A list of the designated University employees who, as counselors, confidential advisors, and medical professionals, do not have this reporting responsibility and can maintain confidentiality, can be found here.
Other information about resources and reporting is available here.
Any student who has suppressed their directory information pursuant to Family Educational Rights and Privacy Act (FERPA) should self-identify to the instructor to ensure protection of the privacy of their attendance in this course. See this page for more information on FERPA.