Simulation-based estimators of analytically intractable causal effects
Simulation-based estimators of analytically intractable causal effects
复制标题
DOI：
10.1111/biom.13499
复制
发表时间：
2021-06-06
期刊：
影响因子：
1.9
通讯作者：
Linero, Antonio R.
中科院分区：
文献类型：
--
作者：
Linero, Antonio R.
在因果推理问题中，人们经常被要求估计因果效应，这是分析上难以处理的数据生成机制的泛函。相关的设置包括估计意向治疗效果的纵向问题与缺失数据或计算直接和间接的影响，中介分析。计算这些效应的一种方法是使用通过蒙特卡罗积分实现的g公式;当使用基于模拟的方法（如非参数自举或马尔可夫链蒙特卡罗）进行推理时，蒙特卡罗积分必须嵌套在已经计算密集的算法中。我们开发了一种广泛适用的方法来加速这一蒙特卡罗积分步骤，大大降低了现有的g-计算算法的计算负担。我们将我们的方法称为加速g计算（AGC）。我们提出的算法是类似的精神，多重插补，但需要删除内插补方差的标准误差，而不是addingit. We说明使用AGC的调解分析问题，使用β回归模型和纵向临床试验受到nonconstituable missingness使用贝叶斯添加剂回归树模型。
In causal inference problems, one is often tasked with estimating causal effects which are analytically intractable functionals of the data-generating mechanism. Relevant settings include estimating intention-to-treat effects in longitudinal problems with missing data or computing direct and indirect effects in mediation analysis. One approach to computing these effects is to use the g-formula implemented via Monte Carlo integration; when simulation-based methods such as the nonparametric bootstrap or Markov chain Monte Carlo are used for inference, Monte Carlo integration must be nested within an already computationally intensive algorithm. We develop a widely-applicable approach to accelerating this Monte Carlo integration step which greatly reduces the computational burden of existing g-computation algorithms. We refer to our method as accelerated g-computation (AGC). The algorithms we present are similar in spirit to multiple imputation, but require removing within-imputation variance from the standard error rather than adding it. We illustrate the use of AGC on a mediation analysis problem using a beta regression model and in a longitudinal clinical trial subject to nonignorable missingness using a Bayesian additive regression trees model.