Sani Numair, Lee Jaron J R, Shpitser Ilya
Dept. of Computer Science, Johns Hopkins University, Baltimore, MD 21218.
Proc Mach Learn Res. 2020 Aug;124:949-958.
Causal inference quantifies cause effect relationships by means of counterfactual responses had some variable been artificially set to a constant. A more refined notion of manipulation, where a variable is artificially set to a fixed function of its natural value is also of interest in particular domains. Examples include increases in financial aid, changes in drug dosing, and modifying length of stay in a hospital. We define counterfactual responses to manipulations of this type, which we call shift interventions. We show that in the presence of multiple variables being manipulated, two types of shift interventions are possible. Shift interventions on the treated (SITs) are defined with respect to natural values, and are connected to effects of treatment on the treated. Shift interventions as policies (SIPs) are defined recursively with respect to values of responses to prior shift interventions, and are connected to dynamic treatment regimes. We give sound and complete identification algorithms for both types of shift interventions, and derive efficient semi-parametric estimators for the mean response to a shift intervention in a special case motivated by a healthcare problem. Finally, we demonstrate the utility of our method by using an electronic health record dataset to estimate the effect of extending the length of stay in the intensive care unit (ICU) in a hospital by an extra day on patient ICU readmission probability.
因果推断通过假设某些变量被人为设定为常数时的反事实响应来量化因果关系。在特定领域中，一种更精细的操纵概念也很有意义，即把一个变量人为设定为其自然值的固定函数。例子包括增加经济援助、改变药物剂量以及调整住院时间。我们定义了对这类操纵的反事实响应，我们称之为转移干预。我们表明，在存在多个被操纵变量的情况下，可能存在两种类型的转移干预。对已治疗者的转移干预（SITs）是相对于自然值定义的，并且与治疗对已治疗者的效果相关。作为政策的转移干预（SIPs）是相对于对先前转移干预的响应值递归定义的，并且与动态治疗方案相关。我们给出了这两种类型转移干预的合理且完整的识别算法，并在一个由医疗保健问题驱动的特殊情况下，推导出了对转移干预平均响应的有效半参数估计量。最后，我们通过使用电子健康记录数据集来估计医院重症监护病房（ICU）住院时间延长一天对患者ICU再入院概率的影响，展示了我们方法的实用性。
