谁增加了急诊科的使用？来自俄勒冈州健康保险实验的新见解

Estimating population average treatment effects from experiments with noncompliance

Randomized control trials (RCTs) are the gold standard for estimating causal effects, but often use samples that are non-representative of the actual population of interest. We propose a reweighting method for estimating population average treatment effects in settings with noncompliance. Simulations show the proposed compliance-adjusted population estimator outperforms its unadjusted counterpart when compliance is relatively low and can be predicted by observed covariates. We apply the method to evaluate the effect of Medicaid coverage on health care use for a target population of adults who …

Detecting Heterogeneous Treatment Effect with Instrumental Variables

There is an increasing interest in estimating heterogeneity in causal effects in randomized and observational studies. However, little research has been conducted to understand heterogeneity in an instrumental variables study. In this work, we present a method to estimate heterogeneous causal effects using an instrumental variable approach. The method has two parts. The first part uses subject-matter knowledge and interpretable machine learning techniques, such as classification and regression trees, to discover potential effect modifiers. The second part uses closed testing to test for the st…

解读人口统计学因素对急诊科儿科患者治疗的影响

Deciphering the influence of demographic factors on the treatment of pediatric patients in the emergency department

本研究通过美国某儿科急诊33万余次就诊记录的回顾性分析及ML模型，证实儿科急诊治疗存在多环节人口统计学差异，且多种就诊特征会影响潜意识偏差及ML决策工具。

超越平均值：不完全依从下的分布因果推断

Beyond the Average: Distributional Causal Inference under Imperfect Compliance

本文提出一种基于工具变量和分布回归的估计器，用于在不完全依从的随机实验中估计局部分布处理效应，达到半参数效率界，并在俄勒冈健康保险实验中验证其有效性。

使用具有多重结果的面板数据进行个体因果推断

Individual Causal Inference Using Panel Data With Multiple Outcomes

提出基于交互固定效应模型的个体层面因果推断方法，利用含多重结果的短面板数据处理未观测个体特征导致的内生性问题，并通过模拟和俄勒冈健康保险实验验证其有效性。

A Nonparametric Likelihood Approach for Inference in Instrumental Variable Models

Instrumental variable methods allow for inference about the treatment effect by controlling for unmeasured confounding in randomized experiments with noncompliance. However, many studies do not consider the observed compliance behavior in the testing procedure, which can lead to a loss of power. In this paper, we propose a novel nonparametric likelihood approach, referred to as the binomial likelihood (BL) method, that incorporates information on compliance behavior while overcoming several limitations of previous techniques and utilizing the advantages of likelihood methods. Our proposed meth…