计算技术与自动化2026，Vol.45Issue(2)：10-16,7.DOI:10.16339/j.cnki.jsjsyzdh.202602002
基于独立权重估计二元处理平均因果效应算法
Algorithm for Estimating Average Causal Effect of Binary Treatment Based on Independent Weight Estimation
摘要
Abstract
In binary treatment,weighted methods are commonly used to estimate the average causal effect.Propensity scores,which make covariates independent of the grouping variable,are widely used in traditional methods.But the accuracy of the propensity score model affects the estimation of the average causal effect.To address this issue,an independent weight determination method is proposed.By quantifying the independence of covariates in each group after weighting through sample moments,the determination of weights is transformed into a goal programming problem.Obtaining inde-pendent weights can balance the differences between groups and does not rely on a specific propensity score model.A sensi-tivity analysis method for estimating the average causal effect using independent weights is also provided.Numerical simula-tions show that the deviation range of the average causal effect estimated by independent weights is concentrated,and the re-sults are robust.On the IHDP dataset,the estimation accuracy reaches 99.16％,with a deviation of 0.0338.Compared with traditional methods,this method has the smallest deviation,the highest accuracy,is insensitive to the selection of ini-tial weight parameters,and the results are robust.关键词
因果推断/独立权重/二元处理/平均因果效应Key words
causal inference/independent weights/binary processing/average causal effect分类
数理科学引用本文复制引用
张雨轩,朱永忠,张洪滔,夏世源..基于独立权重估计二元处理平均因果效应算法[J].计算技术与自动化,2026,45(2):10-16,7.基金项目
国家自然科学基金重点项目(71831006) （71831006）
