广义倾向得分匹配(generalized propensity score)中文学习资料和stata命令操作求助

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向论坛里的朋友求助:最近学习广义倾向得分匹配，需要一些关于其学习的资料，可支付100论坛币购买。另外，如果有关于gpscore和doseresponse命令的实际dofiles操作文件的话，可以支付150论坛币购买（更希望有dofiles和相关论文的实证结果）。希望与高手多交流。（注释：我目前只有三篇中文论文，其余的sj的文件也有若干。）

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关键词：Generalized propensity Generalize stata命令 General 中文学习 资料

企鹅8217 发表于 2015-11-1 19:48
向论坛里的朋友求助:最近学习广义倾向得分匹配，需要一些关于其学习的资料，可支付100论坛币购买。另外，如 ...

好深奥

企鹅8217 发表于 2015-11-1 19:48
向论坛里的朋友求助:最近学习广义倾向得分匹配，需要一些关于其学习的资料，可支付100论坛币购买。另外，如 ...

好深奥

2011阳光照 发表于 2015-11-1 21:58
好深奥

就是普通的求助，深奥在哪里？

不是提问深奥，是你的问题“广义倾向得分匹配”啊。多看看文献吧

2011阳光照 发表于 2015-11-6 08:31
不是提问深奥，是你的问题“广义倾向得分匹配”啊。多看看文献吧

有看文献，也看sj的文章了。有些不懂的地方想交流而已。

倾向值分析--统计方法与应用 (美)郭申阳//马克·W.弗雷泽|译者:郭志刚//巫锡炜
作     者(美)郭申阳//马克·W.弗雷泽|译者:郭志刚//巫锡炜
出 版 社重庆大学
出版时间2012-6-1
ＩＳＢＮ9787562466222

  USING PROPENSITY SCORES IN QUASI-EXPERIMENTAL DESIGNS

William M. Holmes

STATA COMMANDS FOR PROPENSITY USE



            Shenyang Guo and Mark W. Fraser (2010) have written an excellent book on propensity score analysis that provides many Stata commands. While their treatment is more technical than this book, the Stata commands are quite useful. The support site for their book is:  http://ssw.unc.edu/psa/.  The Stata Journal article by Becker and Ichino (2002) provides a good overview of propensity score use and extensive commands with Stata. It can be accessed at http://www.stata-journal.com/sjpdf.html?articlenum=st0026.



IMBALANCE ASSESSMENT

            Imbalance may be assessed with the ANOVA command or ttest.

            

            . anova confounder1 treatment



            .ttest confounder 1 treatment



PROPENSITY ESTIMATION

            Propensities may be estimated with logistic, probit, or linear regression or with discriminant function analysis commands when combined with the predict command.



. logistic confounder1 confounder2 confounder3 i.treatment

. predict propen, xb



. probit treatment confounder1 confounder2 confounder3

. predict propen



. regress treatment confounder1 confounder2 confounder3

. predict propen



. candisc confounder1 confounder2 confounder3, group(treatment)

. predict propen



MATCHING

            The psmatch2 program provides a means for propensity score matching within Stata.  Alternative matching programs may be accessed with the R interface for Stata.



            Exact and Coarsened Exact

            See psmatch2 within Stata



            Nearest Neighbor and Caliper

            See psmatch2 within Stata



            1-Many

            See psmatch2 within Stata



            Optimized, Full, or Genetic

            To do optimized, full, or genetic matching within Stata, the R interface must be used to access the corresponding R programs.



STRATIFYING

            Creating propensity strata can be done with the recode command.



            recode propen 0/.19999=1 .2/.39999=2 .4/.59999=3 .6/.79999=4 .8/.99999=5, gen(propenstrata)

            



REGRESSION AND THE GENERAL LINEAR MODEL

            Regression and General Linear Model estimation may be done with the regress and glm commands.



             . regress outcome1  i.treatment



            . glm outcome1 i.treatment, family(gaussian) link(identity)



TWO-STAGE LEAST SQUARES

            2SLS regression can be done with the ivregress command.



. ivregress 2sls outcome1(treatment = instrument1) , vce(robust)



SAMPLE WEIGHTING

Sample weighting is done within the analytical commands using   pweight. For example with a logit analysis, the command would be:



logit treatment confounder1 confounder2 [pweight=ipa]



WEIGHTED LEAST SQUARES

            Weighted Least Squares may be done within analytical program (see sample weighting above) or with the wls0 command.



.wls0 outcome treatment, wvar(ipa) type(abse) noconst graph



GENERALIZED LINEAR MODEL



            . glm income educ jobexp i.black, family(gaussian) link(identity)



MISSING DATA ANALYSIS

            Analysis of missing data can combine mdesc with mvpatterns or with the misstable command.



            mdesc confounder1 confounder2 confounder3         



            mvpatterns confounder1 confounder2 confounder3



            . misstable summarize



IMPUTATION OF MISSING DATA

            Multiple imputation can be done with the mi impute command. Variables to be imputed must first be registered.  The example below imputes 5 values with a random seed of 123. Estimation of imputed data in Stata may be done with the mi estimate command.

            . mi register imputed confounder1 confounder2

            . mi impute regress confounder1  confounder2 confounder3, add(5) rseed(123)



            . mi estimate: logit treatment confounder1 confounder2 confounder3

            

return to Home                                   

Revised 1/31/2013                                                        
http://www.faculty.umb.edu/william_holmes/propensitystata.htm

Propensity Score Analysis: Statistical Methods and Applications, Second Edition，这本书专门有一章讨论以及提供了一些命令示例，但是还是有点不清晰，我最近也在学习，不太懂

周唯实 发表于 2016-2-29 23:23
Propensity Score Analysis: Statistical Methods and Applications, Second Edition，这本书专门有一章讨论 ...

请移步gpscore2命令。加油！我已完成论文许久，而忙于学习机器学习，淡忘已不少知识，还请您多看论坛。