Model and variable selection for causal inference

因果推理的模型和变量选择

• 批准号: RGPIN-2016-06295
• 负责人: Talbot, Denis
• 金额: $ 1.46万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=677219
• 关键词: Model; variable; selection; causal; inference

To unbiasedly estimate a causal effect utilizing observational data, one must adjust for potentially confounding covariates. Identifying confounding variables using only substantive prior knowledge can be a very difficult task, especially in new areas of research or those where current subject-matter knowledge is sparse. An intuitive strategy to circumvent this problem is to adjust for all potential confounders in a statistical model. This strategy can, however, produce causal exposure effect estimators whose variance is unnecessarily large. Instead, model selection can be attempted. Recent simulation results have shown that classical statistical approaches for model selection often fail to produce unbiased causal effect estimators. Hence, there is a need for the creation of new model selection approaches specifically tailored for causal inference purposes.******I have recently proposed a novel model selection approach, the Bayesian causal effect estimation (BCEE) algorithm, for the estimation of the average causal effect of an exposure variable on a continuous outcome. The results from the simulation study I conducted were very promising since BCEE generally achieved at least some reduction of the mean squared error (MSE) of the causal exposure effect estimator as compared to estimators produced by either a fully adjusted model or by alternative model selection approaches. Notable MSE reduction was observed in some scenarios. Moreover, BCEE produced well calibrated confidence intervals. To facilitate the utilization of BCEE in practice, I also produced an R package that is freely available on the Internet. ******The main objective of my research program is to develop novel, easy to use, model selection approaches in a causal inference framework. My first focus will be to provide extensions of BCEE that would allow for binary, count and survival outcomes. I will also develop approaches that target the causal effect of the exposure in specific subpopulations. Afterward, I will produce methods that are robust to model misspecification (e.g., so-called double robustness property). Many other extensions are possible in the long term, such as developing methods for the estimation of the effect of an exposure regime in a longitudinal setup.******Since model selection in a causal inference framework is a relatively novel area of research, much remains to be done. My research seeks to provide part of the answer to this need.*****

为了利用观察数据无偏地估计因果效应，必须调整潜在的混杂协变量。仅使用大量先验知识来识别混杂变量可能是一项非常困难的任务，特别是在新的研究领域或当前主题知识稀少的领域。规避这个问题的一个直观策略是在统计模型中调整所有潜在的混杂因素。然而，这种策略可以产生因果暴露效应估计量，其方差是不必要的大。相反，可以尝试模型选择。最近的模拟结果表明，经典的模型选择的统计方法往往不能产生无偏的因果效应估计。因此，需要创建专门针对因果推理目的的新模型选择方法。我最近提出了一种新的模型选择方法，贝叶斯因果效应估计（BCEE）算法，用于估计暴露变量对连续结果的平均因果效应。我进行的模拟研究的结果是非常有希望的，因为BCEE一般实现了至少有一些减少的因果暴露效应估计量的均方误差（MSE）相比，由一个完全调整的模型或替代模型选择方法产生的估计。在某些情况下观察到MSE显著降低。此外，BCEE产生良好校准的置信区间。为了便于在实践中使用BCEE，我还制作了一个R软件包，可以在互联网上免费获得。* 我的研究计划的主要目标是在因果推理框架中开发新颖，易于使用的模型选择方法。我的第一个重点将是提供BCEE的扩展，允许二进制，计数和生存结果。我还将开发针对特定亚群暴露的因果效应的方法。之后，我将产生对模型错误指定具有鲁棒性的方法（例如，所谓双鲁棒性）。 从长远来看，许多其他扩展是可能的，例如开发用于估计纵向设置中曝光制度的影响的方法。由于因果推理框架中的模型选择是一个相对新颖的研究领域，还有很多工作要做。我的研究旨在为这一需求提供部分答案。