论文标题
使用仪器变量的内生性学习政策学习
Policy Learning under Endogeneity Using Instrumental Variables
论文作者
论文摘要
本文研究了以内源治疗选择和仪器变量的可用性为特征的观察数据设置中个性化干预策略的识别和估计。我们介绍了操纵工具的鼓励规则。将边际治疗效应（MTE）纳入政策不变的结构参数，我们确定了社会福利标准以实现最佳鼓励规则。为了关注二元鼓励规则，我们建议通过经验福利最大化（EWM）方法估算最佳政策，并得出遗憾的融合率（福利损失）。我们考虑扩展以适应多种工具和预算限制。使用印尼家庭生活调查中的数据，我们应用EWM鼓励规则来建议最佳的学费补贴任务。我们的框架提供了有关为什么针对某个亚种群的解释性。
This paper studies the identification and estimation of individualized intervention policies in observational data settings characterized by endogenous treatment selection and the availability of instrumental variables. We introduce encouragement rules that manipulate an instrument. Incorporating the marginal treatment effects (MTE) as policy invariant structural parameters, we establish the identification of the social welfare criterion for the optimal encouragement rule. Focusing on binary encouragement rules, we propose to estimate the optimal policy via the Empirical Welfare Maximization (EWM) method and derive convergence rates of the regret (welfare loss). We consider extensions to accommodate multiple instruments and budget constraints. Using data from the Indonesian Family Life Survey, we apply the EWM encouragement rule to advise on the optimal tuition subsidy assignment. Our framework offers interpretability regarding why a certain subpopulation is targeted.
