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 全球衛生博士學位學程 |
授課教師 |
黃彥棕 |
課號 |
MGH7033 |
課程識別碼 |
853EM0330 |
班次 |
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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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核心能力關聯 |
核心能力與課程規劃關聯圖 |
課程大綱
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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.
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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.
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課程要求 |
待補 |
預期每週課後學習時數 |
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Office Hours |
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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. |
參考書目 |
Tyler J. VanderWeele. Explanation in Causal Inference: methods for mediation and interaction. Oxford University Press. 2015 (optional)
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評量方式 (僅供參考) |
No. |
項目 |
百分比 |
說明 |
1. |
Midterm |
40% |
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2. |
Final exam |
50% |
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3. |
Class participation |
10% |
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週次 |
日期 |
單元主題 |
第1週 |
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A definition of causal effect |
第2週 |
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Randomized experiments |
第3週 |
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Observational studies |
第4週 |
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Effect modification |
第5週 |
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[no class] |
第6週 |
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Interaction |
第7週 |
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Graphical representation of causal effects |
第8週 |
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Confounding |
第9週 |
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Selection bias |
第10週 |
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IP weighting and marginal structural models |
第11週 |
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Standardization and the parametric g-formula |
第12週 |
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G-estimation and structural nested models |
第13週 |
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Introduction to mediation |
第14週 |
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Multi-mediator models |
第15週 |
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Hypothesis tests of mediation |
第16週 |
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Instrumental variable |
第17週 |
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[no class] |
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