The Best of Both Worlds: Combining Randomized Controlled Trials with Structural Modeling
论证了简化形式（如随机对照试验）与结构建模方法可以协同互补，通过实例展示如何利用RCT数据增强结构估计的可信度，并说明结构方法如何评估反事实政策，最后综述了经济学各子领域中结合这两种方法的研究。
There is a long-standing debate about the extent to which economic theory should inform econometric modeling and estimation. This debate is particularly evident in the program/policy evaluation literature, where reduced-form (experimental or quasi-experimental) and structural modeling approaches are often viewed as rival methodologies. Reduced-form proponents criticize the assumptions invoked in structural applications. Structural modeling advocates point to the limitations of reduced-form approaches in not being able to inform about program impacts prior to implementation or about the costs and benefits of program designs that deviate from the one that was implemented. In this paper, we argue that there is a new emerging view of a natural synergy between these two approaches, that they can be melded to exploit the advantages and ameliorate the disadvantages of each. We provide examples of how data from randomized controlled trials (RCTs), the exemplar of reduced form practitioners, can be used to enhance the credibility of structural estimation. We also illustrate how the structural approach complements experimental analyses by enabling evaluation of counterfactual policies/programs. Lastly, we survey many recent studies that combine these methodologies in various ways across different subfields within economics.