A subvector of predictor that satisfies the ignorability assumption, whose index set is called a sufficient adjustment set, is crucial for conducting reliable causal inference based on observational data. In this paper, we propose a general family of methods to detect all such sets for the first time in the literature, with no parametric assumptions on the outcome models and with flexible parametric and semiparametric assumptions on the predictor within the treatment groups; the latter induces desired sample-level accuracy. We show that the collection of sufficient adjustment sets can uniquely facilitate multiple types of studies in causal inference, including sharpening the estimation of average causal effect and recovering fundamental connections between the outcome and the treatment hidden in the dependence structure of the predictor. These findings are illustrated by simulation studies and a real data example at the end.

翻译：满足可忽略性假设的预测变量子向量（其索引集称为充分调整集）对于基于观测数据进行可靠的因果推断至关重要。本文首次在文献中提出了一种通用方法族，用于检测所有此类集合，该方法不对结果模型作参数假设，且对处理组内的预测变量采用灵活的参数与半参数假设；后者能产生所需的样本级精度。我们证明，充分调整集的集合能够独特地促进因果推断中的多种研究类型，包括提高平均因果效应的估计精度，以及揭示隐藏在预测变量依赖结构中的结果与处理之间的基本联系。这些发现通过模拟研究和文末的真实数据示例得到了验证。