Class: Monday and Wednesday 1:30-2:45 pm
- Room: HH 120
Instructor: Marc Dotson
- Contact: [email protected]
- Office Hours: Monday and Wednesday 3-5 pm, or by appointment, EBB 705 or Zoom
TA: Holden Nielson
- Contact: [email protected]
- Office Hours: By appointment
This course focuses on the application of regression to inform decision-making, particularly using interpretable models to understand the effect of interventions on business outcomes. Students learn to model experimental and observational data and infer causality instead of correlation only.
By the end of this course, you will be able to:
- Specify identification strategies for estimating causal effects.
- Design effective experiments and apply appropriate methods for experimental data.
- Model observational data and infer causality using a variety of techniques.
This course is heavily focused on skill-building. Each student will need to bring a laptop, either their own or one rented from USU. All assignments and project work will be completed using GitHub, an online version control hub powered by Git. Walk through this training to get setup with Python, Positron, Git and GitHub, and Quarto.
We will be studying Kevin Murphy’s excellent Probabilistic Machine Learning series. Free PDFs of his books are available here:
Other free materials will be provided as needed.
Students should consider the following study tips.
- Prepare for class by studying the assigned materials, taking notes, and coming with questions.
- Take notes, ask questions, and participate in class discussions.
- Consistently apply what you’re learning to your project.
- Work with classmates and utilize office hours.
- Use your class repository to organize all notes and project work.
Letter grades will follow the standard rubric.
| A | 93-100% | B- | 80-82% | D+ | 67-69% |
| A- | 90-92% | C+ | 77-79% | D | 63-66% |
| B+ | 87-89% | C | 73-76% | D- | 60-62% |
| B | 83-86% | C- | 70-72% | E | 0-59% |
Grades will be determined as follows.
| Participation | 50% |
| Project | 50% |
No credit will be given for late work unless an arrangement is made prior to the deadline. You are encouraged to review your graded work and ask questions to avoid repeated mistakes.
This class is all about participation. If you aren’t attending, you can’t contribute. You will take turns preparing slides and presenting to lead the discussion each class. When relevant, please include relevant code when leading the discussion.
The project gives students the opportunity to demonstrate mastery over the topics. Students will select what they want to study and work iteratively on the project throughout the semester, applying techniques and understanding as they acquire it. There will be an intermediate presentation on the project halfway through through the semester and a final presentation at the end of the semester. The project log of milestones along with the final presentation slide deck will both be part of your project grade.
Create a separate branch for each week’s work, merging to the main
branch and deleting the merged branch by the deadline each Saturday
night. Points will be lost for not following this workflow each week.
Please note that I reserve the right to change the syllabus, including the schedule, at any time and for any reason. I will give advance notice as it effects any deadlines.
- January 6: Python and Positron (Marc)
- January 8: GitHub and Quarto (Marc)
- Probabilistic Machine Learning: An Introduction Chapter 1
- Milestone 1: Draft Project Idea
- January 13: Univariate Models (Rebecca)
- January 15: Multivariate Models (Marc)
- Probabilistic Machine Learning: An Introduction Chapters 2 and 3
- Milestone 2: Narrate the Data Story
- January 20: No Class (MLK, Jr. Day)
- January 22: Structural Causal Models (Abby)
- Probabilistic Machine Learning: Advanced Topics Chapter 36.1-36.2
- Milestone 3: Specify a DAG
- January 27: Elemental Confounds (Alesandro)
- January 29: Good and Bad Controls (Jonah)
- Probabilistic Machine Learning: Advanced Topics Chapter 36.3-36.4
- Milestone 4: Specify an Identification Strategy
- February 3: Bayesian Statistics (Gabby)
- February 5: Frequentist Statistics (Rebecca)
- Probabilistic Machine Learning: An Introduction Chapter 4
- Milestone 5: Simulate Data and Recover Parameters
- February 10: Bayesian Decision Theory (Abby)
- February 12: Frequentist Decision Theory (Alesandro)
- Probabilistic Machine Learning: An Introduction Chapter 5
- Milestone 6: Conduct Exploratory Data Analysis
- February 17: No Class (President’s Day)
- February 19: Introduction to PyMC (Marc)
- Milestone 7: Estimate Causal Effects
- February 24: Presentations
- February 26: Presentations
- Milestone 8: Intermediate Presentation Slides
- March 3: Experimental Control
- March 5: Conjoint
- Milestone 9: Run Conjoint Experiment
- March 10: No Class (Spring Break)
- March 12: No Class (Spring Break)
- March 17: Choice Modeling
- March 19: Estimating Hierarchical Models
- Milestone 10: Estimate Heterogeneous Effects
- March 24: Approximating Experimental Control
- March 26: Diff-in-Diff
- Milestone 11: Implement Diff-in-Diff Strategy
- March 31: Matching
- April 2: Meta-Learning
- Milestone 12: Implement Synthetic Controls
- April 7: Doubly Robust Machine Learning
- April 9: Other Methods
- Milestone 13: Implement Causal Machine Learning
- April 14: Final Presentations
- April 16: Final Presentations
- Milestone 14: Final Presentations
- April 21: What’s Next for Causal Inference?
- Milestone 15: Course Feedback
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Utah State University supports the principle of freedom of expression for both faculty and students. The University respects the rights of faculty to teach and students to learn. Maintenance of these rights requires classroom conditions that do not impede the learning process. Disruptive classroom behavior will not be tolerated. An individual engaging in such behavior may be subject to disciplinary action. Read Student Code Article V Section V-3 for more information.
Academic freedom is the right to teach, study, discuss, investigate, discover, create, and publish freely. Academic freedom protects the rights of faculty members in teaching and of students in learning. Freedom in research is fundamental to the advancement of truth. Faculty members are entitled to full freedom in teaching, research, and creative activities, subject to the limitations imposed by professional responsibility. Faculty Code Policy#403 further defines academic freedom and professional responsibilities.
Each student has the right and duty to pursue his or her academic experience free of dishonesty. To enhance the learning environment at Utah State University and to develop student academic integrity, each student agrees to the following Honor Pledge:
“I pledge, on my honor, to conduct myself with the foremost level of academic integrity.”
A student who lives by the Honor Pledge is a student who does more than not cheat, falsify, or plagiarize. A student who lives by the Honor Pledge:
- Espouses academic integrity as an underlying and essential principle of the Utah State University community;
- Understands that each act of academic dishonesty devalues every degree that is awarded by this institution; and
- Is a welcomed and valued member of Utah State University.
The instructor of this course will take appropriate actions in response to Academic Dishonesty, as defined the University’s Student Code. Acts of academic dishonesty include but are not limited to:
Cheating: using, attempting to use, or providing others with any unauthorized assistance in taking quizzes, tests, examinations, or in any other academic exercise or activity. Unauthorized assistance includes:
- Working in a group when the instructor has designated that the quiz, test, examination, or any other academic exercise or activity be done “individually;”
- Depending on the aid of sources beyond those authorized by the instructor in writing papers, preparing reports, solving problems, or carrying out other assignments;
- Substituting for another student, or permitting another student to substitute for oneself, in taking an examination or preparing academic work;
- Acquiring tests or other academic material belonging to a faculty member, staff member, or another student without express permission;
- Continuing to write after time has been called on a quiz, test, examination, or any other academic exercise or activity;
- Submitting substantially the same work for credit in more than one class, except with prior approval of the instructor; or engaging in any form of research fraud.
Falsification: altering or fabricating any information or citation in an academic exercise or activity.
Plagiarism: representing, by paraphrase or direct quotation, the published or unpublished work of another person as one‘s own in any academic exercise or activity without full and clear acknowledgment. It also includes using materials prepared by another person or by an agency engaged in the sale of term papers or other academic materials.
For additional information go to: ARTICLE VI. University Regulations Regarding Academic Integrity.
USU strives to provide an environment for students and employees that is free from discrimination and sexual misconduct. If you experience sexual misconduct or discrimination at any point during the semester inside or outside of class, you are encouraged to contact the USU Title IX Coordinator via Distance Education room 400 in Logan, 435-797-1266, [email protected], or at equity.usu.edu/report. You can learn more about the USU resources available for individuals who have experienced sexual misconduct at sexualrespect.usu.edu. Resources for individuals who have experienced discrimination are listed at equity.usu.edu/resources.
USU cares about our students and provides a number of resources and supportive measures to students who may be experiencing thoughts of self-harm or who have experienced sexual misconduct. To ensure students are informed about resources and services available to them, including available grievance or criminal processes for incidents of sexual misconduct, USU has implemented reporting policies and practices that require designated employees to report any information they receive about incidents of sexual misconduct. This reporting policy also assists USU with its efforts to prevent sexual misconduct and keep our campus community safe.
Under USU’s sexual misconduct reporting policy, I am designated as a “reporting employee.” This means that if you share information with me about incidents of sexual misconduct (sexual harassment, sexual assault, relationship violence, or sex-based stalking), including within a course assignment, I will report that information to the USU Title IX Coordinator. I will also share with you information about designated confidential resources, supportive measures, and how you can file a report with the USU Title IX Coordinator.
Self-disclosures about sexual misconduct that you experienced are not required for your course work.
Similarly, if you disclose thoughts of harm to self or a threat to others to me, including within a course assignment, I will report the information to the appropriate campus administrators. I will also share with you information about the mental health and wellness resources available to you.
Students are required to complete all courses for which they are registered by the end of the semester. In some cases, a student may be unable to complete all of the coursework because of extenuating circumstances, but not due to poor performance or to retain financial aid. The term ‘extenuating’ circumstances includes: (1) incapacitating illness which prevents a student from attending classes for a minimum period of two weeks, (2) a death in the immediate family, (3) financial responsibilities requiring a student to alter a work schedule to secure employment, (4) change in work schedule as required by an employer, or (5) other emergencies deemed appropriate by the instructor.
USU welcomes students with disabilities. If you have, or suspect you may have, a physical, mental health, or learning disability that may require accommodations in this course, please contact the Disability Resource Center (DRC) as early in the semester as possible (University Inn # 101, (435) 797‐2444, [email protected]). All disability related accommodations must be approved by the DRC. Once approved, the DRC will coordinate with faculty to provide accommodations.
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Office of Equity: Distance Education, Room 400, Logan Campus, 435-797-1266, Office of Equity: Pregnancy and Pregnancy Related Conditions.
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Students are also encouraged to download the “SafeUT App” to their smartphones. The SafeUT application is a 24/7 statewide crisis text and tip service that provides real-time crisis intervention to students through texting and a confidential tip program that can help anyone with emotional crises, bullying, relationship problems, mental health, or suicide related issues.
The Student Nutrition Access Center (SNAC) offers free food assistance to all students. Students are welcome to visit the pantry once per calendar week. There are no questions or qualifications required to access this service; you simply need to present your student ID card or A#.
The pantry is committed to supporting student well-being by ensuring access to nutritious food options. For more information, including pantry hours and location, please visit SNAC Food Pantry.
Utilizing the SNAC Food Pantry is a smart way to manage your food needs and stay focused on your academic success.
Disclaimer: This syllabus is subject to change during the semester based on the needs of the class.