论文标题
测试观察数据中因果效应的鉴定
Testing the identification of causal effects in observational data
论文作者
论文摘要
这项研究证明了存在可检验的条件，以鉴定治疗对观测数据中结果的因果作用，这依赖于两组变量：观察到的协变量要控制和可疑的仪器。在经验应用中常见的因果结构下，鉴于治疗和协变量的可疑仪器和结果的可检验条件独立性具有两种影响。首先，该仪器是有效的，即，它不会直接影响结果（除了通过治疗以外），并且在协变量上不受条件。其次，治疗是在协变量上有条件的条件，因此可以确定治疗效果。我们建议基于机器学习方法来测试这种条件独立性，该方法在模拟研究中以数据驱动方式解释了协变量，并研究了其渐近行为和有限样本性能。我们还将测试方法应用于评估生育能力对女性劳动力供应的影响，当使用前两个孩子的同胞性别比作为所谓的仪器，这很大程度上违反了我们对中等社会经济协变量的可检验含义。
This study demonstrates the existence of a testable condition for the identification of the causal effect of a treatment on an outcome in observational data, which relies on two sets of variables: observed covariates to be controlled for and a suspected instrument. Under a causal structure commonly found in empirical applications, the testable conditional independence of the suspected instrument and the outcome given the treatment and the covariates has two implications. First, the instrument is valid, i.e. it does not directly affect the outcome (other than through the treatment) and is unconfounded conditional on the covariates. Second, the treatment is unconfounded conditional on the covariates such that the treatment effect is identified. We suggest tests of this conditional independence based on machine learning methods that account for covariates in a data-driven way and investigate their asymptotic behavior and finite sample performance in a simulation study. We also apply our testing approach to evaluating the impact of fertility on female labor supply when using the sibling sex ratio of the first two children as supposed instrument, which by and large points to a violation of our testable implication for the moderate set of socio-economic covariates considered.
