Data adaptive covariate balancing for causal effect estimation for high dimensional data
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高维数据因果效应估计中的数据自适应协变量平衡
- 研究问题
- 高维观察性数据中，如何让直接加权平衡更重视真正影响处理和结果的混杂变量，同时降低参数模型误设与维度灾难的影响？
- 方法与思路
- 作者用随机森林分别建模处理与结果，以样本落入相同叶节点的比例构造相似度，形成强调相关协变量的分布距离并直接估计权重。
- 材料陈述的贡献
- 摘要称，在适当假设下，权重以 L2 范数收敛到归一化逆倾向得分，并给出一致的处理效应估计；模拟和真实数据展示方法表现。
- 阅读关注点
- 应核对森林相似度如何合并两类模型、平衡优化的约束及重叠性条件，并检查权重收敛如何转化为效应估计和不确定性量化。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未获核实。摘要未列出适当假设、收敛速度和推断程序，不能仅凭模拟描述认定普遍优于现有方法。
原文摘要
A key challenge in estimating causal effects from observational data is handling confounding and is commonly achieved through weighting methods that balance distribution of covariates between treatment and control groups. Weighting approaches can be classified by whether weights are estimated using parametric or nonparametric methods, and by whether the model relies on modeling and inverting the propensity score or directly estimates weights to achieve distributional balance by minimizing a measure of dissimilarity between groups. Parametric methods, both for propensity score modeling and direct balancing, are prone to model misspecification. In addition, balancing approaches often suffer from the curse of dimensionality, as they assign equal importance to all covariates, thus potentially de-emphasizing true confounders. Several methods, such as the outcome adaptive lasso, attempt to mitigate this issue through variable selection, but are parametric and focus on propensity score estimation rather than direct balancing. In this paper, we propose a nonparametric direct balancing approach that uses random forests to adaptively emphasize confounders. Our method jointly models treatment and outcome using random forests, allowing the data to identify covariates that influence both processes. We construct a similarity measure, defined by the proportion of trees in which two observations fall into the same leaf node, yielding a distance between treatment and control distributions that is sensitive to relevant covariates and captures the structure of confounding. Under suitable assumptions, we show that the resulting weights converge to normalized inverse propensity scores in the L2 norm and provide consistent treatment effect estimates. We demonstrate the effectiveness of our approach through extensive simulations and an application to a real dataset.