MIMICKING COUNTERFACTUAL OUTCOMES TO ESTIMATE CAUSAL EFFECTS.
MIMICKING COUNTERFACTUAL OUTCOMES TO ESTIMATE CAUSAL EFFECTS.
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DOI：
10.1214/15-aos1433
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发表时间：
2017-04
影响因子：
4.5
通讯作者：
Lok JJ
中科院分区：
文献类型：
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作者：
Lok JJ
在观察性研究中，治疗可以在没有固定方案的情况下，在连续的时间内多次适应协变量。治疗影响协变量，后者影响治疗，后者影响协变量，等等。那么，即使是时间依赖的COX模型也不能用来估计净治疗效果。已在此设置中应用了结构嵌套模型。结构嵌套模型是基于反事实的：如果一个人的治疗在一定时间后被扣留，结果会是什么。以前关于连续时间结构嵌套模型的工作假设反事实依赖于观测数据，同时猜测这一假设可以放松。本文证明了一个人可以通过构造随机变量来模仿反事实，即使给出了过去的观测数据，这些随机变量也是微分方程式的解，其分布与反事实相同。这些“模拟”变量可用于估计结构嵌套模型的参数，而无需假定处理效果是确定的。
In observational studies, treatment may be adapted to covariates at several times without a fixed protocol, in continuous time. Treatment influences covariates, which influence treatment, which influences covariates, and so on. Then even time-dependent Cox-models cannot be used to estimate the net treatment effect. Structural nested models have been applied in this setting. Structural nested models are based on counterfactuals: the outcome a person would have had had treatment been withheld after a certain time. Previous work on continuous-time structural nested models assumes that counterfactuals depend deterministically on observed data, while conjecturing that this assumption can be relaxed. This article proves that one can mimic counterfactuals by constructing random variables, solutions to a differential equation, that have the same distribution as the counterfactuals, even given past observed data. These “mimicking” variables can be used to estimate the parameters of structural nested models without assuming the treatment effect to be deterministic.