課程資訊
課程名稱
因果推論
Causal Inference 
開課學期
109-1 
授課對象
公共衛生學院  流行病學與預防醫學研究所  
授課教師
黃彥棕 
課號
MGH7033 
課程識別碼
853 M0330 
班次
 
學分
3.0 
全/半年
半年 
必/選修
選修 
上課時間
星期四6,7,8(13:20~16:20) 
上課地點
公衛601 
備註
本課程以英語授課。教室:公衛601A
總人數上限:24人 
Ceiba 課程網頁
http://ceiba.ntu.edu.tw/1091MGH7033_ 
課程簡介影片
 
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課程概述

Causal inference is an emerging field in statistics. It has been rigorously studied under the counterfactual or potential outcome framework proposed by Donald Rubin (1978). Judea Pearl linked the causal inference with graph theory using directed acyclic graphs; James Robins further extend causal inference to the settings where exposure and confounding are time-varying. In addition to the theoretical and methodological development, causal inference analyses have also been widely used in biomedical research.
The first part of this course will introduce mechanisms that affect causal inference, including confounding, selection bias and interaction. Understanding the bias from confounding and selection helps studying causal inference. With knowledge about these biases, one can judge or even adjust for the bias using proper study designs or advanced statistical analyses. We will also introduce directed acyclic graph and study the biases using the graph theory.
The second part will introduce g-methods, including marginal structural models, G-formula/standardization and structural nested model/G-estimation. Based on the concept from the first part, we will learn how to construct statistical models that can account for the undue bias (confounding and/or selection bias).
The third part will introduce causal mediation model. Causal mediation analyses have received much attention recently. While the conventional causal inference concerns the causal relationship between an exposure (A) and the health outcome (Y), causal mediation model further characterizes the mechanism that mediates the causation. Specifically, causal mediation model studies the effect of A on Y mediated through a mediator (M), A->M->Y. Finally, we will introduce a relevant topic: instrumental variable analyses and discuss its required assumptions and how such an analyses provides a remedy for unmeasured confounding. 

課程目標
(1) Introducing concepts of causal inference, including counterfactual outcome、directed acyclic graph.
(2) Introducing statistical analyses such as inverse probability weighting and standardization to uncover causal effects.
(3) Introducing definitions of causal mediation and its required identifiability assumptions.
(4) Understanding how to formulate scientific hypothesis using causal mediation model and to conduct causal mediation analyses.
(5) Discussing challenges of causal inference in aspects of methodology and application. 
課程要求
待補 
Office Hours
 
參考書目
Tyler J. VanderWeele. Explanation in Causal Inference: methods for mediation and interaction. Oxford University Press. 2015 (optional) 
指定閱讀
1. Causal Inference, by Miguel A. Hernan and James M. Robins (unpublished book; attached in the course website)
2. VanderWeele, T. J. and Vansteelandt, S. Conceptual issues concerning mediation, intervention and composition. Statistics and its Inference 2009, 2:457-468.
3. Imai, K., Keele, L., and Yamamoto, T. Identification, inference and sensitivity analysis for causal mediation effects. Statistical Science 2010, 25: 51–71.
4. VanderWeele TJ and Vansteelandt S. Odds ratios for mediation analysis for a dichotomous outcome. American Journal of Epidemiology 2010, 172: 1339-1349.
5. Lai, E. Y., Shih, S., Huang, Y. T. and Wang, S. A mediation analysis for a nonrare dichotomous outcome with sequentially ordered multiple mediators. Statistics in Medicine 2020, 39:1415-1428.
6. Shih, S., Huang, Y. T. and Yang, H. I. A multiple mediator analysis approach to quantify the effects of the ADH1B and ALDH2 genes on hepatocellular carcinoma risk. Genetic Epidemiology 2018, 42:394-404.
7. Huang, Y. T. and Cai, T. Mediation analysis for survival data using semiparametric probit models. Biometrics 2016, 72: 563-574.
8. Huang, Y. T. and Yang, H. I. Causal mediation analysis of survival outcome with multiple mediators. Epidemiology 2017, 28: 370-378.
9. Huang, Y. T. and Pan, W. C. Hypothesis Test of Mediation Effect in Causal Mediation Model with High-dimensional Continuous Mediators. Biometrics 2016, 72: 402-413.
10. Huang, Y. T. Joint significance tests for mediation effects of socioeconomic adversity on adiposity via epigenetics. Annals of Applied Statistics 2018, 12:1535-1557.
11. Huang, Y. T. Genome-wide analyses of sparse mediation effects under composite null hypotheses. Annals of Applied Statistics 2019, 13:60-84.
12. Huang, Y. T. Variance component tests of multivariate mediation effects under composite null hypotheses. Biometrics 2019, 75:1191-1204.
13. Didelez, V. and Sheehan, N. Mendelian randomization as a instrumental variable approach to causal inference. Statistical Methods in Medical Research 2007, 16:309-330.
14. Huang, Y. T. Mendelian randomization using semiparametric linear transformation models. Statistics in Medicine 2020, 39:890-905. 
評量方式
(僅供參考)
 
No.
項目
百分比
說明
1. 
Midterm 
40% 
 
2. 
Final exam 
50% 
 
3. 
Class participation 
10% 
 
 
課程進度
週次
日期
單元主題
第1週
  A definition of causal effect 
第2週
  Randomized experiments 
第3週
  Observational studies 
第4週
  Effect modification 
第5週
  [no class] 
第6週
  Interaction 
第7週
  Graphical representation of causal effects 
第8週
  Confounding 
第9週
  Selection bias 
第10週
  IP weighting and marginal structural models 
第11週
  Standardization and the parametric g-formula 
第12週
  G-estimation and structural nested models 
第13週
  Introduction to mediation 
第14週
  Multi-mediator models 
第15週
  Hypothesis tests of mediation 
第16週
  Instrumental variable 
第17週
  [no class]