The Nonparametric Identification of Treatment Effects in Duration Models
分析因果多变量持续时间模型的设定与识别，聚焦于处理开始时间对结果持续时间的影响，在不依赖随机分配和排除限制的条件下，利用事件发生时间信息识别处理效应。
This paper analyzes the specification and identification of causal multivariate duration models. We focus on the case in which one duration concerns the point in time a treatment is initiated and we are interested in the effect of this treatment on some outcome duration. We define "no anticipation of treatment" and relate it to a common assumption in biostatistics. We show that (i) no anticipation and (ii) randomized treatment assignment can be imposed without restricting the observational data. We impose (i) but not (ii) and prove identification of models that impose some structure. We allow for dependent unobserved heterogeneity and we do not exploit exclusion restrictions on covariates. We provide results for both single-spell and multiple-spell data. The timing of events conveys useful information on the treatment effect.