【主题】 | Data-driven Policy Learning for a Continuous Treatment |
【报告人】 | 解海天（助理教授，北京大学） |
【时间】 | 2024年5月28日周二09:30-11:00 |
【地点】 | 经济学院702会议室 |
【语言】 | 英文 |
【主持人】 | 张征宇教授 |
【摘要】 | This paper studies policy learning under the condition of unconfoundedness with a continuous treatment variable. Our research begins by employing kernel-based inverse propensity-weighted (IPW) methods to estimate policy welfare. We aim to approximate the optimal policy within a global policy class characterized by infinite Vapnik-Chervonenkis (VC) dimension. This is achieved through the utilization of a sequence of sieve policy classes, each with finite VC dimension. Preliminary analysis reveals that welfare regret comprises of three components: global welfare deficiency, variance, and bias. This leads to the necessity of simultaneously selecting the optimal bandwidth for estimation and the optimal policy class for welfare approximation. To tackle this challenge, we introduce a semi-data-driven strategy that employs penalization techniques. This approach yields oracle inequalities that adeptly balance the three components of welfare regret without prior knowledge of the welfare deficiency. By utilizing precise maximal and concentration inequalities, we derive sharper regret bounds than those currently available in the literature. In instances where the propensity score is unknown, we adopt the doubly robust (DR) moment condition tailored to the continuous treatment setting. In alignment with the binary-treatment case, the DR welfare regret closely parallels the IPW welfare regret, given the fast convergence of nuisance estimators. |
【报告人简介】 | 解海天，2023年毕业于美国加州大学圣地亚哥分校。主要研究方向为因果推断理论，包括工具变量、断点回归等因果推断方法的非参数/半参数识别与估计，以及基于因果模型的政策分析评估、政策学习与统计决策等。研究成果发表于Journal of Business and Economic Statistics, Journal of Econometrics, Oxford Bulletin of Economics and Statistics等国际期刊。 |
