在大数据时代，观察性研究（observational studies）已经成为科学研究中推断因果关系的重要数据来源。观察性数据中往往存在未观测混杂因素（unmeasured confounder）和缺失数据，这会导致因果推断的偏差和决策的错误。工具变量方法是调整未观测混杂因素的最为有效的方法之一，被广泛用于经济、金融、医学和生物研究。然而，工具变量的有效性无法从观测数据验证，基于工具变量的因果推断经常受到质疑。本次短期课程将回顾观察性研究的几个基础方法，并介绍新的有效工具变量方法、代理推断、非随机缺失数据分析方法，及其在生物医学、流行病学和社会经济中的应用案例。
主讲人：
郭子剑，副教授，Rutgers University
苗旺，助理教授，北京大学
时间：6月24日至6月27日，9:00-12:00，共四次课，
地点：浙江大学数据科学研究中心，行政楼1417
本次短期课程计划从以下方面讨论有关观察性研究的因果推断：
第一部分：因果推断基础，工具变量，弱和无效工具变量
1.因果推断的基础：从potential outcome 和 structural equation model 介绍 unmeasured confounder bias 以及其影响。
2.工具变量的基础：工具变量的假设以及two stage least square estimator，control function。 (Chapter 5 of 【1】 and 【2】)
3.弱工具变量（weak instrumental variable）的统计推断【3】：concentration parameters，Anderson-Rubin test, Conditional Likelihood Ratio test。
4.在存在无效工具变量（invalid instrumental variable）时进行有效推断【4】
5.工具变量选择导致的post-selection inference 及其解决办法【5】
6.高维内生变量相关的统计推断【6】
7.机器学习方法在观察性研究下的有效使用【7】
8.工具变量方法在经济学和医学中(包括但不限于孟德尔随机化)的具体应用以及软件使用【8，9】
第二部分：代理推断，合成对照和缺失数据分析
1．完全观测的混杂因素的调整
2. 重差法与合成对照 [10]
3. 代理推断 [11-13]
4. 基于代理推断的合成对照、阴性测试设计[14-15]
5. 非随机缺失数据的识别性和双稳健推断[16-18]
References
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[12] Shi, X., W. Miao, J. C. Nelson, and E. Tchetgen Tchetgen (2020). Multiply robust causal inference with double negative control adjustment for categorical unmeasured confounding. Journal of the Royal Statistical Society: Series B 82, 521–540
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