ASSESSING LACK OF COMMON SUPPORT IN CAUSAL INFERENCE USING BAYESIAN NONPARAMETRICS: IMPLICATIONS FOR EVALUATING THE EFFECT OF BREASTFEEDING ON CHILDREN'S COGNITIVE OUTCOMES
ASSESSING LACK OF COMMON SUPPORT IN CAUSAL INFERENCE USING BAYESIAN NONPARAMETRICS: IMPLICATIONS FOR EVALUATING THE EFFECT OF BREASTFEEDING ON CHILDREN'S COGNITIVE OUTCOMES
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DOI：
10.1214/13-aoas630
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发表时间：
2013-09-01
影响因子：
1.8
通讯作者：
Su, Yu-Sung
中科院分区：
文献类型：
--
作者：
Hill, Jennifer;Su, Yu-Sung
观察性研究中的因果推断通常需要在不同的组之间进行比较。例如，研究人员在调查延长母乳喂养时间对儿童结局的作用时，可能被迫对平均特征差异很大的妇女进行比较。在极端情况下，协变量空间可能存在邻域，其中两组女性（长期母乳喂养的女性和未长期母乳喂养的女性）的数量都不足以对这些女性进行推断。这被称为缺乏共同支持。当我们试图估计缺乏共同支持的单位的因果效应时，可能会出现问题，因此我们可能希望避免对此类单位的推断。如果对一组潜在混杂因素的可验证性得到满足，那么确定共同支持假设是否成立或对哪些单位成立是一个经验问题。然而，在高维协变量空间中，通常需要满足可扩展性，这样的识别可能不是微不足道的。用于解决这个问题的现有方法通常需要依赖于参数假设，并且大多数（如果不是全部）忽略了响应变量中嵌入的信息。我们区分“共同支持”和“共同因果支持”的概念。“我们提出了一种新的方法来确定共同的因果支持，解决了现有方法的一些缺点。我们使用全国青年纵向调查的数据来激励和说明这种方法，以估计母乳喂养至少9个月对5岁或6岁时阅读和数学成绩的影响。我们还评估了这种方法的比较性能在假设的例子和模拟的真实治疗效果是已知的。
Causal inference in observational studies typically requires making comparisons between groups that are dissimilar. For instance, researchers investigating the role of a prolonged duration of breastfeeding on child outcomes may be forced to make comparisons between women with substantially different characteristics on average. In the extreme there may exist neighborhoods of the covariate space where there are not sufficient numbers of both groups of women (those who breastfed for prolonged periods and those who did not) to make inferences about those women. This is referred to as lack of common support. Problems can arise when we try to estimate causal effects for units that lack common support, thus we may want to avoid inference for such units. If ignorability is satisfied with respect to a set of potential confounders, then identifying whether, or for which units, the common support assumption holds is an empirical question. However, in the high-dimensional covariate space often required to satisfy ignorability such identification may not be trivial. Existing methods used to address this problem often require reliance on parametric assumptions and most, if not all, ignore the information embedded in the response variable. We distinguish between the concepts of "common support" and "common causal support." We propose a new approach for identifying common causal support that addresses some of the shortcomings of existing methods. We motivate and illustrate the approach using data from the National Longitudinal Survey of Youth to estimate the effect of breastfeeding at least nine months on reading and math achievement scores at age five or six. We also evaluate the comparative performance of this method in hypothetical examples and simulations where the true treatment effect is known.