因果推論
Causal Inference
| 節 | 週五 |
|---|---|
5 13:20–14:10 | 因果推論 A406 3 節連堂 |
6 14:20–15:10 | |
7 15:30–16:20 |
* 根據陽明交大上課時間表所列
Course description: This course (IST5556: causal inference and mediation analysis) introduce methods for causal inference, mediation analysis and interaction analysis. The course begins with presenting the three basic techniques widely used in causal theory development: (1) Counterfactual (potential) outcome model, (2) Direct Acyclic Graphs, and (3) Sufficient Component Cause model; and then explored in specific topics such as time-varying system, mediation analysis, interaction analysis, and different methods for estimation. Students who take this course are expected to be equipped with sufficient techniques to develop causal theory in a certain topic and conduct data analysis based on causal model. Course Objectives: 1. Understand the framework of studies in field of causal theory, mediation analysis and interaction analysis. 2. Formulate and address causal problems based on counterfactual models, causal diagram, and sufficient component cause model 3. Estimate causal effect and conduct mediation analysis using various modeling approaches 4. Identify the advantages and disadvantages of causal inference method and traditional statistical analysis.
No prerequisites for this course, but familiarity with basic statistical inference and regression model will be assumed. Some exposure to data analysis with statistics software (especially R) will be helpful.
Teaching Assistant: Meng-Ying Chou, PhD Candidate
Six homework assignment (30% of the final grade) Midterm exam (20%) Final exam (20%) Six homework assignment (30% of the final grade) Students are encourage to discuss and work on the homework assignment in groups. Exams are in-class and close book, almost based from the homework assignments.
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to causal inference Counterfactual outcome model Identification issues for causal effects |
| 第 2 週 | Peace Memorial Day |
| 第 3 週 | Causal Diagram (Direct Acyclic Graphs, DAGs) D-separation rules Two sources of system error: confounding and selection bias |
| 第 4 週 | Adjustment for confounding and selection bias Stratification and regression model Standardization and g-method |
| 第 5 週 | Estimation for causal effects: (1) parametric formula (2) Inverse probability weighting estimation and marginal structural model (3) Monte-Carlo Simulation method |
| 第 6 週 | Time-varying system g-formula Inverse probability weighting estimation in time-varying system |
| 第 7 週 | Tomb Sweeping Day |
| 第 8 週 | Introduction to mechanism investigation in data science Traditional mediation analysis under linearity Causal mediation analysis |
| 第 9 週 | Estimation for mediation effect: regression-based models and Monte-Carlo Simulation. |
| 第 10 週 | Introduction to interaction analysis Sufficient Component Cause Model Mechanistic interaction |
| 第 11 週 | Midterm Exam |
| 第 12 週 | Sufficient Component Cause model for epidemiologic studies |
| 第 13 週 | Mediation analysis under time-varying system Mediational g-formula Investigation the mechanism of religious behaviors on health |
| 第 14 週 | Final Project |
| 第 15 週 | Mediation analysis on genomic data |
| 第 16 週 | Dragon Boat Festival |
| 第 17 週 | Final exam |
| 第 18 週 | Course review Feedback and suggestions |
No required reading for this course. Recommended Reading List will be distributed in the first few classes.
- 地點
- Room 415, Assembly Building I
- 時間
- Wed 12:00 am to 1:00 pm or by appointment. Please inform the instructor one day in advance if you would like to use the office hour.
- 聯絡方式
- shenglin@stat.nctu.edu.tw (03-5712121 ext: 56822)