Title: DFW: A Novel Weighting Scheme for Covariate Balancing and Treatment Effect Estimation Show Chinese title

Title: DFW：一种用于协变量平衡和处理效应估计的新加权方案

Abstract: 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. Show Chinese abstract

Abstract: 从观察数据中估计因果效应具有挑战性，这是由于选择偏差导致处理组之间的协变量分布不平衡。基于倾向得分的加权方法被广泛用于通过重新加权样本以模拟随机对照试验（RCT）来解决这个问题。然而，这些方法的有效性在很大程度上取决于观察到的数据和倾向得分估计器的准确性。例如，逆倾向得分加权（IPW）根据倾向得分的倒数分配权重，当倾向得分具有高方差时（无论是由于数据还是模型误指）会导致不稳定的权重，最终降低处理选择偏差和处理效应估计的能力。为了克服这些限制，我们提出了去混淆因子加权（DFW），这是一种新颖的基于倾向得分的方法，利用去混淆因子来构建稳定有效的样本权重。DFW优先考虑较少混淆的样本，同时减轻高度混淆样本的影响，生成一个更接近RCT的伪总体。我们的方法确保权重有界、方差较低，并改善协变量平衡。虽然DFW是为二元处理设计的，但它自然可以扩展到多处理设置，因为去混淆因子是基于每个样本实际接受的处理的估计概率计算的。通过在现实世界基准和合成数据集上的大量实验，我们证明DFW在协变量平衡和处理效应估计方面优于现有方法，包括IPW和CBPS。