Bayesian Inference for Sequential Treatments Under Latent Sequential Ignorability
Bayesian Inference for Sequential Treatments Under Latent Sequential Ignorability
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潜在顺序可忽略性下顺序处理的贝叶斯推理
DOI：
10.1080/01621459.2019.1623039
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发表时间：
2016
影响因子：
3.7
通讯作者：
F. Mealli
中科院分区：
文献类型：
--
作者：
F. Ricciardi;Alessandra Mattei;F. Mealli
摘要：我们专注于纵向治疗的因果推理，在多个时间点将单位分配给治疗，旨在评估不同治疗序列对最终点观察到的结果的影响。类似研究中的一个常见假设是序贯可验证性（SI）：假设每个时间点的治疗分配独立于过去观察到的结局和协变量的未来潜在结局。当治疗参与取决于个人选择，治疗分配可能取决于与未来结局相关的不可观察的数量时，SI是值得怀疑的。我们依赖于主要分层，制定一个宽松的版本SI：潜在的序贯可验证性（LSI）假设，治疗分配是有条件地独立于未来的潜在结果，过去的治疗，协变量，和主要层成员，一个潜在的变量定义的联合值观察和缺失的中间结果。我们评估SI和LSI，使用理论参数和模拟研究，以调查两个假设的性能时，一个持有和推理下进行。仿真结果表明，当SI不成立时，在SI下进行的推理会导致误导性的结论。相反，LSI通常会导致正确的后验分布，无论哪个假设成立。
Abstract We focus on causal inference for longitudinal treatments, where units are assigned to treatments at multiple time points, aiming to assess the effect of different treatment sequences on an outcome observed at a final point. A common assumption in similar studies is sequential ignorability (SI): treatment assignment at each time point is assumed independent of future potential outcomes given past observed outcomes and covariates. SI is questionable when treatment participation depends on individual choices, and treatment assignment may depend on unobservable quantities associated with future outcomes. We rely on principal stratification to formulate a relaxed version of SI: latent sequential ignorability (LSI) assumes that treatment assignment is conditionally independent on future potential outcomes given past treatments, covariates, and principal stratum membership, a latent variable defined by the joint value of observed and missing intermediate outcomes. We evaluate SI and LSI, using theoretical arguments and simulation studies to investigate the performance of the two assumptions when one holds and inference is conducted under both. Simulations show that when SI does not hold, inference performed under SI leads to misleading conclusions. Conversely, LSI generally leads to correct posterior distributions, irrespective of which assumption holds.