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  • 學位論文

自我選擇下的最適處置分配

Optimal Treatment Allocation under Self-Selection

指導教授 : 管中閔
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摘要


在實證研究中通常可以觀察到處置效果會隨著個人的特性有異質性,因此根據個人特性決定要給予哪個處置的處置規則隨之受到來自各領域的注意。這篇論文研究在內生處置下並且有工具變數時如何分配處置的問題。這裡所考慮的處置規則只能決定誰會被鼓勵去接受處置。我們提供了認定的條件以及估計的方法。跟 Athey and Wager (2021) 比較,他們在這個背景下研究的處置規則可以直接改變人們的決定。另一方面,我們也指出當研究者想要考量處置成本的時候,有兩種方式可以引入我們的方法。由於研究方式以及處置成本的兩者選擇都需要根據問題而決定,我們討論了一些例子並指出這些例子中可以幫助研究者們決定的因素。

並列摘要


Heterogeneous treatment effects due to observable characteristics is ubiquitous in program evaluation. Given the heterogeneity, finding a treatment rule that makes the treatment decision according to observable features has received growing attention. This thesis studies the treatment allocation problem under binary endogenous treatment with a binary instrument. The type of treatment rules considered in this thesis can only determine who should be encouraged to take treatment. We provide the identification conditions as well as the method to derive the treatment rule. Furthermore, we compare our method with the one proposed by Athey and Wager (2021) which targets the treatment rule of treatment status. In addition, we discuss two different ways to incorporate treatment cost into our method. Since both the choices of the analysis approach and the specification of cost will depend on the context of the problem, we give some examples and identify the key features in these examples that may help practitioners to decide their approaches.

參考文獻


Abadie, A. (2003). Semiparametric instrumental variable estimation of treatment response models. Journal of Econometrics, 113(2), 231-263.
Abrevaya, J., Hsu, Y.­C., Lieli, R. P. (2015). Estimating conditional average treatment effects. Journal of Business and Economic Statistics, 33(4), 485­-505.
Athey, S., Imbens, G. (2016). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113(27), 7353–7360.
Athey, S., Tibshirani, J., Wager, S. (2019, 04). Generalized random forests. Ann. Statist., 47(2), 1148–1178.
Athey, S., Wager, S. (2019). Estimating treatment effects with causal forests: An application. Observational Studies, 5(2), 37–51.

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