Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap
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去混杂得分：弱重叠下因果效应估计的特征表示
- 研究问题
- 当处理组与对照组协变量重叠不足时，能否通过特征表示改善重叠，并避免降维或变量选择引入目标效应的混杂偏差？
- 方法与思路
- 作者提出去混杂得分，给出可由观测数据识别的零协方差条件，并在简化的高斯协变量情形刻画一类得分和模拟估计量。
- 材料陈述的贡献
- 摘要称，这类表示可改善重叠而不改变估计目标，简单模拟展示良好有限样本性质，可能替代逆概率和均衡权重的常规正则化。
- 阅读关注点
- 应核对零协方差条件与消除目标偏差之间的逻辑、得分的可构造性，以及高斯简化设定之外的适用范围和估计误差。
- 阅读边界
- 这是 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要仅给出简化设定与概念验证，未说明一般情形的完整保障。
原文摘要
A key condition for obtaining reliable estimates of the causal effect of a treatment is overlap (a.k.a. positivity): the distributions of the features used to perform causal adjustment cannot be too different in the treated and control groups. In cases where overlap is poor, causal effect estimators can become brittle, especially when they incorporate weighting. To address this problem, a number of proposals (including confounder selection or dimension reduction methods) incorporate feature representations to induce better overlap between the treated and control groups. A key concern in these proposals is that the representation may introduce confounding bias into the effect estimator. In this paper, we introduce deconfounding scores, which are feature representations that induce better overlap without biasing the target of estimation. We show that deconfounding scores satisfy a zero-covariance condition that is identifiable in observed data. As a proof of concept, we characterize a family of deconfounding scores in a simplified setting with Gaussian covariates, and show that in some simple simulations, these scores can be used to construct estimators with good finite-sample properties. In particular, we show that this technique could be an attractive alternative to standard regularizations that are often applied to IPW and balancing weights.