提出实用变量选择方法，提升因果推断在有限数据下的准确性。
Practically Effective Adjustment Variable Selection in Causal Inference
- 基于后门准则筛选候选变量，避免因变量选择不当导致偏差。
- 在真实数据和人工数据上验证，显著减少因果估计误差。
- 适用于不完全已知因果图的场景，适合实际研究者使用。
在因果效应估计中，常用方法是调整满足后门准则的变量以消除混杂因素影响。然而，往往无法唯一确定满足该准则的变量集，且现实数据通常有限，可能导致统计估计不足。为此，本文提出一套变量选择标准及相应算法，防止因果效应估计精度下降。研究首先聚焦于有向无环图（DAG），随后给出在已完成的部分有向无环图（CPDAG）上应用该方法的具体步骤，并证明了在CPDAG上计算因果效应的可能性定理。最后，通过现有数据与人工数据验证了所提方法的实用性。
原文摘要 · Abstract (English)
In the estimation of causal effects, one common method for removing the influence of confounders is to adjust the variables that satisfy the back-door criterion. However, it is not always possible to uniquely determine sets of such variables. Moreover, real-world data is almost always limited, which means it may be insufficient for statistical estimation. Therefore, we propose criteria for selecting variables from a list of candidate adjustment variables along with an algorithm to prevent accuracy degradation in causal effect estimation. We initially focus on directed acyclic graphs (DAGs) and then outlines specific steps for applying this method to completed partially directed acyclic graphs (CPDAGs). We also present and prove a theorem on causal effect computation possibility in CPDAGs. Finally, we demonstrate the practical utility of our method using both existing and artificial data.
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