DFW: A Novel Weighting Scheme for Covariate Balancing and Treatment Effect Estimation

TL;DR

This paper introduces DFW, a new weighting scheme for covariate balancing that improves treatment effect estimation by producing stable, less confounded sample weights, outperforming existing methods in experiments.

Contribution

The paper proposes DFW, a novel propensity score-based weighting method that constructs stable weights using deconfounding factors, enhancing covariate balance and treatment effect estimation.

Findings

DFW achieves better covariate balance than existing methods.

DFW produces lower variance weights and more stable estimates.

DFW outperforms IPW and CBPS in experiments.

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.