Identification in Nonparametric Models for Dynamic Treatment Effects
Identification in Nonparametric Models for Dynamic Treatment Effects
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动态治疗效果的非参数模型识别
DOI：
10.2139/ssrn.3182800
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Sukjin Han
中科院分区：
文献类型：
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作者：
Sukjin Han
本文开发了一种非参数模型，该模型表示结果序列和治疗选择如何以动态方式相互影响。在这种情况下，我们有兴趣确定每个时期个体的平均结果，如果分配了特定的治疗顺序。该数量的确定使我们能够确定平均治疗效果（ATE）和过渡时的 ATE，以及最佳治疗方案，即最大化平均潜在结果（加权）总和的方案，可能会减少治疗成本。本文的主要贡献是通过为一系列内源性处理引入灵活的选择理论框架，放宽生物统计学文献中广泛使用的序贯随机化假设。我们表明，感兴趣的参数是在每个时期的双向排除限制下确定的，即，将工具排除在结果确定过程之外，将其他外生变量排除在治疗选择过程之外。在后面的变量不可用的情况下，我们还考虑部分识别。最后，我们将结果扩展到并非每个时期都出现治疗的环境。
This paper develops a nonparametric model that represents how sequences of outcomes and treatment choices influence one another in a dynamic manner. In this setting, we are interested in identifying the average outcome for individuals in each period, had a particular treatment sequence been assigned. The identification of this quantity allows us to identify the average treatment effects (ATE's) and the ATE's on transitions, as well as the optimal treatment regimes, namely, the regimes that maximize the (weighted) sum of the average potential outcomes, possibly less the cost of the treatments. The main contribution of this paper is to relax the sequential randomization assumption widely used in the biostatistics literature by introducing a flexible choice-theoretic framework for a sequence of endogenous treatments. We show that the parameters of interest are identified under each period's two-way exclusion restriction, i.e., with instruments excluded from the outcome-determining process and other exogenous variables excluded from the treatment-selection process. We also consider partial identification in the case where the latter variables are not available. Lastly, we extend our results to a setting where treatments do not appear in every period.