DFW: A Novel Weighting Scheme for Covariate Balancing and Treatment Effect Estimation
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DFW：用于协变量平衡与处理效应估计的新加权方案
- 研究问题
- 观察性研究中，倾向得分加权可能产生不稳定权重；如何改善协变量平衡，并降低这种不稳定性对处理效应估计的影响？
- 方法与思路
- 作者提出基于倾向得分与去混杂因子的DFW，降低高度混杂样本的权重，并在合成数据和真实基准数据上比较。
- 材料陈述的贡献
- 摘要称，DFW提供有界权重、更低方差及更好的平衡，实验优于IPW和CBPS，并可由二元处理扩展至多处理。
- 阅读关注点
- 应核对去混杂因子的定义、加权后的目标人群及估计对象，检查所谓优于其他方法的比较条件和倾向模型误设情形。
- 阅读边界
- 这是arXiv预印本，同行评审及期刊接收未核实；摘要未说明完整识别条件，权重稳定或平衡改善不能单独保证因果有效性。
原文摘要
Estimating causal effects from observational data is challenging due to selection bias, which leads to imbalanced covariate distributions across treatment groups. Propensity score-based weighting methods are widely used to address this issue by reweighting samples to simulate a randomized controlled trial (RCT). However, the effectiveness of these methods heavily depends on the observed data and the accuracy of the propensity score estimator. For example, inverse propensity weighting (IPW) assigns weights based on the inverse of the propensity score, which can lead to instable weights when propensity scores have high variance-either due to data or model misspecification-ultimately degrading the ability of handling selection bias and treatment effect estimation. To overcome these limitations, we propose Deconfounding Factor Weighting (DFW), a novel propensity score-based approach that leverages the deconfounding factor-to construct stable and effective sample weights. DFW prioritizes less confounded samples while mitigating the influence of highly confounded ones, producing a pseudopopulation that better approximates a RCT. Our approach ensures bounded weights, lower variance, and improved covariate balance.While DFW is formulated for binary treatments, it naturally extends to multi-treatment settings, as the deconfounding factor is computed based on the estimated probability of the treatment actually received by each sample. Through extensive experiments on real-world benchmark and synthetic datasets, we demonstrate that DFW outperforms existing methods, including IPW and CBPS, in both covariate balancing and treatment effect estimation.