论文标题
结合观察性和实验数据以使用不完美的仪器提高效率
Combining Observational and Experimental Data to Improve Efficiency Using Imperfect Instruments
论文作者
论文摘要
随机对照试验产生的实验变化可以可靠地识别因果效应，但经常遭受有限的规模，而观察数据集则大，但通常会违反所需的识别假设。为了提高估计效率，我提出了一种利用不完美工具的方法 - 预处理协变量满足相关条件，但可能违反了排除限制。我表明，这些不完美的工具可用于得出矩限制，这些仪器与实验数据结合提高了估计效率。我概述了实施此策略的估计量，并表明我的方法可以将差异降低多达50％；因此，仅需要一半的实验样本才能达到相同的统计精度。我将我的方法应用于Expedia的搜索清单数据集，该数据集研究了搜索排名对点击的因果影响，并证明该方法可以大大提高精度。
Randomized controlled trials generate experimental variation that can credibly identify causal effects, but often suffer from limited scale, while observational datasets are large, but often violate desired identification assumptions. To improve estimation efficiency, I propose a method that leverages imperfect instruments - pretreatment covariates that satisfy the relevance condition but may violate the exclusion restriction. I show that these imperfect instruments can be used to derive moment restrictions that, in combination with the experimental data, improve estimation efficiency. I outline estimators for implementing this strategy, and show that my methods can reduce variance by up to 50%; therefore, only half of the experimental sample is required to attain the same statistical precision. I apply my method to a search listing dataset from Expedia that studies the causal effect of search rankings on clicks, and show that the method can substantially improve the precision.
