class: center, middle, inverse, title-slide .title[ # L0: Course Orientation ] .author[ ### Jean Morrison ] .institute[ ### University of Michigan ] .date[ ### Lecture on 2025-01-08 (updated: 2025-01-06) ] --- `\(\newcommand{\ci}{\perp\!\!\!\perp}\)` ## Welcome + Course Basics `\(\newcommand{\ci}{\perp\!\!\!\perp}\)` + We will meet Monday and Wednesday 1-2:30 opm in G850 SPH I. + Textbook: - ["What If?" By Miguel Hernán and James Robins](https://miguelhernan.org/whatifbook) - Other readings will be provided as pdfs + Office Hours: - Monday 3-4pm in-person in Jean's office (M4148) or the Conference Room (M4034) - You can request to join by Zoom. I won't run Zoom if there are no requests. - By appointment --- ## Course Structure Tour of [Canvas site](https://umich.instructure.com/courses/727344) --- ## What is Causal Inference? + Causal inference is the art and science of using statistical tools to answer causal questions. -- + Interpreting statistical parameters as causal parameters requires: - A language and philosophy of causality. - A model of the system you are studying. --- ## What We Will Learn in this Class -- + Languages of causality: - Counterfactuals and Potential Outcomes - Graphical models - Structural Equation Models -- + Identifiability conditions: When would a parameter be estimable with infinite data? -- + Estimation strategies: - Weighting, standardization, G-estimation - Propensity scores and matching - Instrumental variable analysis - Methods for time-varying treatment - `\(\dots\)` --- ## About Me - Jean Morrison (call me Jean, Dr. Morrison is also ok) (they/them). -- - Research Interests: + Statistical genetics + Instrumental variable analysis with genetic instruments + High dimensional biological data + Data integration (combining multiple sources of Hi-D data) + Applications of ML tools in causal inference. -- - I like animals and outdoor activities (hiking, biking, kayaking, etc). --- ## About This Class - My goal is to create an active, collaborative, learning environment. - Ground rules: + Be engaged, ask questions, and contribute to discussions. + Complete assignments on schedule. + Be respectful of me and your classmates. - Please feel free to contact me if something is not working for you or you are struggling. --- ## Attendance and Recording - I will run Zoom during class, so you may attend remotely if you cannot be here in person. + I will not monitor the chat. + You cannot receive points for the in-class activities remotely. - All classes will be recorded and available on the Canvas site. - Recordings will be available only to other students in this course. --- ## Due Dates and Late Policy - You are strongly encouraged to meet the deadlines laid out in the course schedule. - However, if something prevents you from meeting these deadlines, you will not be penalized as long as you contact me ahead of time to arrange an alternate deadline. + Please email at least two days in advance (not counting weekends). E.g. if the deadline is on Monday, email me by Thursday. - There are three exceptions to this policy: + The progress report must be handed in on time because it will be reviewed by your peers. Allowing late submissions would not be fair to others who need time to read your work. + The final project report is due on 4/17. I have limited flexibility on this date due to end of semester grading constraints. + Final presentations occur on 4/22 and 4/29, the last two days of class. --- ## Intro Survey Please fill out [the introductory survey](https://forms.gle/Hc8oKxEz8FmMHxEQ7) by Friday 1/10.