主题: Identification and Estimation of Marginal Treatment Effects in the Absence of Instrumental Variable

Abstract: 

This paper proposes a method of defining, identifying, and estimating marginal treatment effect (MTE) without imposing instrumental variable assumptions of independence, exclusion, and separability. Under the definition of MTE based on normalized treatment error that is statistically independent of covariates, we find that the relationship between MTE and standard treatment parameters holds as well as in instrumental variable models. We provides a possible set of sufficient conditions ensuring identification of such defined MTE in an environment of essential heterogeneity. The key conditions include a linear restriction on potential outcome regression functions, a nonlinear restriction on propensity score function, and a conditional mean independence restriction that leads to additive separability. We prove identification following the notion of semiparametric identification by functional form. Consistent semiparametric estimation procedures are suggested, and an empirical application to Head Start is provided to illustrate the usefulness of the proposed method in analyzing heterogenous causal effects when instruments are elusive.

报告时间：2023年7月25日下午16:30

线下地点：威廉希尔6-210会议室

腾讯会议ID: 221-728-420

嘉宾简介：

潘哲文，william hill中文网副研究员，2017年毕业于中山大学岭南学院，获数量经济学博士学位。研究领域为微观计量经济学理论与应用，包括受限因变量模型、因果推断方法等，研究成果发表于《Journal of Business & Economic Statistics》、《Journal of Futures Market》、《中国科学：数学》、《管理科学学报》、《统计研究》等国内外核心期刊，主持国家自然科学基金青年项目和面上项目。