Evaluating treatment protocols using data combination
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利用数据组合评估处理分配规则
- 研究问题
- 当处理分配使用了研究者未观测的协变量时，如何判断分配规则是否低效，并识别边际接受者的反事实收益分布？
- 方法与思路
- 作者提出容许未观测选择的部分识别方法，并将观测数据与实验估计组合以点识别所需反事实分布，应用于冠状动脉手术。
- 材料陈述的贡献
- 摘要称，方法可检测分配低效并推断相关风险偏好；美国案例中，控制年龄后，吸烟者须达到更高预期生存门槛才能获手术。
- 阅读关注点
- 应核对低效判据、部分识别界的构造、实验与观测数据组合的条件，以及生存门槛差异能支持何种分配机制解释。
- 阅读边界
- 仅据所给出版社摘要，无法判断数据组合的完整识别条件和门槛差异大小；该案例结果不能单独确定偏好动机或推广到其他医疗场景。
原文摘要
In real life, individuals are often assigned to binary treatments according to existing treatment protocols. Such protocols, when designed with “taste-based” motives, would be productively inefficient in that the expected returns to treatment for a marginal treatment recipient would vary across covariates and be larger for discriminated groups. This cannot be directly tested if assignment is based on more covariates than the researcher observes, because then the marginal treatment recipient is not identified. We present (i) a partial identification approach to detecting such inefficiency which is robust to selection on unobservables and (ii) a novel way of point-identifying the necessary counterfactual distributions by combining observational datasets with experimental estimates. These methods can also be used to (partially) infer risk-preferences which may rationalize the observed treatment allocations. Specifically, existing healthcare datasets can be analyzed with the proposed tools to test the allocational efficiency of medical treatments. Using our methodology on data from the Coronary Artery Surgery Study in the US, which combined experimental and observational components, we find that after controlling for age, smokers in the observational dataset had to overcome a higher threshold of expected survival relative to nonsmokers in order to qualify for surgery.