倾向得分匹配-双重差分模型(PSM⁃DID)是政策评估及因果推断中最为流行的方法之一.但是在实际应用中,该方法面临着控制变量在处理组样本和控制组样本之间非平衡性的挑战.传统基于均值差异t检验的平衡性检验容易产生片面和误导性的结论,...倾向得分匹配-双重差分模型(PSM⁃DID)是政策评估及因果推断中最为流行的方法之一.但是在实际应用中,该方法面临着控制变量在处理组样本和控制组样本之间非平衡性的挑战.传统基于均值差异t检验的平衡性检验容易产生片面和误导性的结论,使得后续因果推断产生偏误.为克服上述问题,本文对传统的平衡性检验提出以下改进:一是推荐更全面的多维度的平衡性测度指标,便于在匹配后更严谨地比较处理组和控制组的平衡性;二是提出了适用于非平衡样本的新估计方法:倾向得分匹配-逆概率加权-双重差分(PSM⁃IPW⁃DID),该方法结合了倾向得分匹配(PSM)克服样本自选择内生性及对非平衡样本稳健的优势和逆概率加权(inverse probability weighting,IPW)利用全样本信息的长处,在不进一步删除样本的情况下得到一种更稳健的双重差分估计方法.数据模拟和应用实例显示,本文提出的新方法能更全面、客观地评价宏观、微观政策的作用,得到更为可信的因果推断.展开更多
In this article, to improve the doubly robust estimator, the nonlinear regression models with missing responses are studied. Based on the covariate balancing propensity score (CBPS), estimators for the regression coef...In this article, to improve the doubly robust estimator, the nonlinear regression models with missing responses are studied. Based on the covariate balancing propensity score (CBPS), estimators for the regression coefficients and the population mean are obtained. It is proved that the proposed estimators are asymptotically normal. In simulation studies, the proposed estimators show improved performance relative to usual augmented inverse probability weighted estimators.展开更多
An improved method for estimation of causal effects from observational data is demonstrated. Applications in medicine have been few, and the purpose of the present study is to contribute new clinical insight by means ...An improved method for estimation of causal effects from observational data is demonstrated. Applications in medicine have been few, and the purpose of the present study is to contribute new clinical insight by means of this new and more sophisticated analysis. Long term effect of medication for adult ADHD patients is not resolved. A model with causal parameters to represent effect of medication was formulated, which accounts for time-varying confounding and selection-bias from loss to follow-up. The popular marginal structural model (MSM) for causal inference, of Robins et al., adjusts for time-varying confounding, but suffers from lack of robustness for misspecification in the weights. Recent work by Imai and Ratkovic?[1][2] achieves robustness in the MSM, through improved covariate balance (CBMSM). The CBMSM (freely available software) was compared with a standard fit of a MSM and a naive regression model, to give a robust estimate of the true treatment effect in 250 previously non-medicated adults, treated for one year, in a specialized ADHD outpatient clinic in Norway. Covariate balance was greatly improved, resulting in a stronger treatment effect than without this improvement. In terms of treatment effect per week, early stages seemed to have the strongest influence. An estimated average reduction of 4 units on the symptom scale assessed at 12 weeks, for hypothetical medication in the 9 - 12 weeks period compared to no medication in this period, was found. The treatment effect persisted throughout the whole year, with an estimated average reduction of 0.7 units per week on symptoms assessed at one year, for hypothetical medication in the last 13 weeks of the year, compared to no medication in this period. The present findings support a strong and causal direct and indirect effect of pharmacological treatment of adults with ADHD on improvement in symptoms, and with a stronger treatment effect than has been reported.展开更多
目的评价真实世界研究(real world study,RWS)组间协变量均衡性的诊断指标。方法模拟不同的组间均衡性程度、不同的协变量与暴露、结局关系等RWS模拟数据场景,通过构建各诊断指标与估计偏差的相关性模型,评价不同的单一协变量、全局协...目的评价真实世界研究(real world study,RWS)组间协变量均衡性的诊断指标。方法模拟不同的组间均衡性程度、不同的协变量与暴露、结局关系等RWS模拟数据场景,通过构建各诊断指标与估计偏差的相关性模型,评价不同的单一协变量、全局协变量均衡性诊断指标的准确性、稳健性。结果除L1测度外,标准化差值法、重叠系数、K-S距离、Lévy距离、马氏距离和一般加权差均能识别不同程度的均衡性。基于倾向得分的C统计量和一般加权差估计相关性模型的R2值均大于0.8,截距值逼近原点,对于组间均衡性的诊断最为准确和稳定。结论单一协变量诊断指标可以评估RWS数据组间协变量的均衡性,但全局诊断指标的准确性、灵敏度和稳健性更好,其中倾向得分C统计量的诊断效果最佳。展开更多
Different covariate balance weighting methods have been proposed by researchers from different perspectives to estimate the treatment effects.This paper gives a brief review of the covariate balancing propensity score...Different covariate balance weighting methods have been proposed by researchers from different perspectives to estimate the treatment effects.This paper gives a brief review of the covariate balancing propensity score method by Imai and Ratkovic(2014),the stable balance weighting procedure by Zubizarreta(2015),the calibration balance weighting approach by Chan,et al.(2016),and the integrated propensity score technique by Sant’Anna,et al.(2020).Simulations are conducted to illustrate the finite sample performance of both the average treatment effect and quantile treatment effect estimators based on different weighting methods.Simulation results show that in general,the covariate balance weighting methods can outperform the conventional maximum likelihood estimation method while the performance of the four covariate balance weighting methods varies with the data generating processes.Finally,the four covariate balance weighting methods are applied to estimate the treatment effects of the college graduate on personal annual income.展开更多
文摘倾向得分匹配-双重差分模型(PSM⁃DID)是政策评估及因果推断中最为流行的方法之一.但是在实际应用中,该方法面临着控制变量在处理组样本和控制组样本之间非平衡性的挑战.传统基于均值差异t检验的平衡性检验容易产生片面和误导性的结论,使得后续因果推断产生偏误.为克服上述问题,本文对传统的平衡性检验提出以下改进:一是推荐更全面的多维度的平衡性测度指标,便于在匹配后更严谨地比较处理组和控制组的平衡性;二是提出了适用于非平衡样本的新估计方法:倾向得分匹配-逆概率加权-双重差分(PSM⁃IPW⁃DID),该方法结合了倾向得分匹配(PSM)克服样本自选择内生性及对非平衡样本稳健的优势和逆概率加权(inverse probability weighting,IPW)利用全样本信息的长处,在不进一步删除样本的情况下得到一种更稳健的双重差分估计方法.数据模拟和应用实例显示,本文提出的新方法能更全面、客观地评价宏观、微观政策的作用,得到更为可信的因果推断.
文摘In this article, to improve the doubly robust estimator, the nonlinear regression models with missing responses are studied. Based on the covariate balancing propensity score (CBPS), estimators for the regression coefficients and the population mean are obtained. It is proved that the proposed estimators are asymptotically normal. In simulation studies, the proposed estimators show improved performance relative to usual augmented inverse probability weighted estimators.
文摘An improved method for estimation of causal effects from observational data is demonstrated. Applications in medicine have been few, and the purpose of the present study is to contribute new clinical insight by means of this new and more sophisticated analysis. Long term effect of medication for adult ADHD patients is not resolved. A model with causal parameters to represent effect of medication was formulated, which accounts for time-varying confounding and selection-bias from loss to follow-up. The popular marginal structural model (MSM) for causal inference, of Robins et al., adjusts for time-varying confounding, but suffers from lack of robustness for misspecification in the weights. Recent work by Imai and Ratkovic?[1][2] achieves robustness in the MSM, through improved covariate balance (CBMSM). The CBMSM (freely available software) was compared with a standard fit of a MSM and a naive regression model, to give a robust estimate of the true treatment effect in 250 previously non-medicated adults, treated for one year, in a specialized ADHD outpatient clinic in Norway. Covariate balance was greatly improved, resulting in a stronger treatment effect than without this improvement. In terms of treatment effect per week, early stages seemed to have the strongest influence. An estimated average reduction of 4 units on the symptom scale assessed at 12 weeks, for hypothetical medication in the 9 - 12 weeks period compared to no medication in this period, was found. The treatment effect persisted throughout the whole year, with an estimated average reduction of 0.7 units per week on symptoms assessed at one year, for hypothetical medication in the last 13 weeks of the year, compared to no medication in this period. The present findings support a strong and causal direct and indirect effect of pharmacological treatment of adults with ADHD on improvement in symptoms, and with a stronger treatment effect than has been reported.
文摘目的评价真实世界研究(real world study,RWS)组间协变量均衡性的诊断指标。方法模拟不同的组间均衡性程度、不同的协变量与暴露、结局关系等RWS模拟数据场景,通过构建各诊断指标与估计偏差的相关性模型,评价不同的单一协变量、全局协变量均衡性诊断指标的准确性、稳健性。结果除L1测度外,标准化差值法、重叠系数、K-S距离、Lévy距离、马氏距离和一般加权差均能识别不同程度的均衡性。基于倾向得分的C统计量和一般加权差估计相关性模型的R2值均大于0.8,截距值逼近原点,对于组间均衡性的诊断最为准确和稳定。结论单一协变量诊断指标可以评估RWS数据组间协变量的均衡性,但全局诊断指标的准确性、灵敏度和稳健性更好,其中倾向得分C统计量的诊断效果最佳。
基金the National Natural Science Foundation of China under Grant Nos.71631004 and 72033008the National Science Foundation for Distinguished Young Scholars under Grant No.71625001the Science Foundation of Ministry of Education of China under Grant No.19YJA910003。
文摘Different covariate balance weighting methods have been proposed by researchers from different perspectives to estimate the treatment effects.This paper gives a brief review of the covariate balancing propensity score method by Imai and Ratkovic(2014),the stable balance weighting procedure by Zubizarreta(2015),the calibration balance weighting approach by Chan,et al.(2016),and the integrated propensity score technique by Sant’Anna,et al.(2020).Simulations are conducted to illustrate the finite sample performance of both the average treatment effect and quantile treatment effect estimators based on different weighting methods.Simulation results show that in general,the covariate balance weighting methods can outperform the conventional maximum likelihood estimation method while the performance of the four covariate balance weighting methods varies with the data generating processes.Finally,the four covariate balance weighting methods are applied to estimate the treatment effects of the college graduate on personal annual income.
