全球智库信息集成服务系统(G2TT): Reinforcing RCTs with Multiple Priors while Learning about External Validity

G2TT

来源类型	Working Paper
规范类型	报告
DOI	10.3386/w29756
来源ID	Working Paper 29756
	Reinforcing RCTs with Multiple Priors while Learning about External Validity
	Frederico Finan; Demian Pouzo
发表日期	2022-02-14
出版年	2022
语种	英语
摘要	This paper presents a framework for how to incorporate prior sources of information into the design of a sequential experiment. These sources can include previous experiments, expert opinions, or the experimenter's own introspection. We formalize this problem using a multi-prior Bayesian approach that maps each source to a Bayesian model. These models are aggregated according to their associated posterior probabilities. We evaluate a broad of policy rules according to three criteria: whether the experimenter learns the parameters of the payoff distributions, the probability that the experimenter chooses the wrong treatment when deciding to stop the experiment, and the average rewards. We show that our framework exhibits several nice finite sample properties, including robustness to any source that is not externally valid.
主题	Econometrics ; Estimation Methods ; Experimental Design ; Development and Growth ; Development
URL	https://www.nber.org/papers/w29756
来源智库	National Bureau of Economic Research (United States)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/587430
推荐引用方式 GB/T 7714	Frederico Finan,Demian Pouzo. Reinforcing RCTs with Multiple Priors while Learning about External Validity. 2022.

条目包含的文件
文件名称/大小	资源类型	版本类型	开放类型	使用许可
w29756.pdf（942KB）	智库出版物		限制开放	CC BY-NC-SA	浏览