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统计计量 | 因果推理的匹配方法及其在Stata中的实现
本文转载自公众号计量经济学服务中心
来源：Barbara Sianesi，IFS，Stata Users’ Group Meeting
匹配，尤其是在倾向评分方面，已经成为一种非常流行的评价方法。事实上，匹配是选择匹配(或重新加权)的比较组的最佳方法，该比较组看起来像感兴趣的治疗组。
在本文中，将在一般的因果推理问题中介绍匹配方法，强调它们的优缺点，并简要概述不同的匹配估计量。然后，使用psmatch2演示Stata中基于真实数据的实际示例。然后，我将展示如何实现其中一些估计，并强调一些实现问题
本文主要包括两部分，第一部分为课件，第二部分为相应的文档代码
Part1因果推理的匹配方法及其在Stata中的实现
Part2对应文档代码
// PSMATCH2 EXAMPLE
use nsw_psid, clear
desc
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ta treated
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// Propensity score
/************************************************************************/
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dprobit treated age black hispanic married educ nodegree re75
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cap drop score
predict double score
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// Common support
/************************************************************************/
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summarize score if treated==1, detail
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summarize score if treated==0, detail
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density2 score, group(treated)
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psgraph, treated(treated) pscore(score) bin(50)
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// Nearest neighbour without replacement without caliper
/************************************************************************/
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psmatch2 treated, pscore(score) outcome(re78) noreplacement
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pstest2 age black hispanic married educ nodegree re75, sum graph
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// Nearest neighbour without replacement within caliper
/************************************************************************/
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psmatch2 treated, pscore(score) outcome(re78) noreplacement caliper(0.01)
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summarize _support if treated==1
display 1-r(mean)
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psgraph
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pstest2 age black hispanic married educ nodegree re75, sum graph
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// Nearest neighbour with replacement
/************************************************************************/
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psmatch2 treated, pscore(score) outcome(re78)
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summarize _pdif, detail
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psmatch2 treated, pscore(score) outcome(re78) caliper(0.01)
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summarize _support if treated==1
display 1-r(mean)
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tab _weight if treated==0
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psgraph
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density2 score, g(treated)
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density2 score, g(treated) m(_weight)
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pstest2 age black hispanic married educ nodegree re75, sum graph
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density2 re75 if re75<40000, g(treated)
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density2 re75 if re75<40000, g(treated) m(_weight)
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// >1-to-1 matching
/************************************************************************/
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psmatch2 treated, pscore(score) outcome(re78) neigh(10)
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psmatch2 treated, pscore(score) outcome(re78) neigh(20)
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psmatch2 treated, pscore(score) outcome(re78) neigh(10) cal(0.01)
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tab _nn if treated==1
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psmatch2 treated, pscore(score) outcome(re78) neigh(20) cal(0.01)
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tab _nn if treated==1
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// Kernel matching
/************************************************************************/
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psmatch2 treated, pscore(score) outcome(re78) kernel k(epan) bw(0.06)
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psmatch2 treated, pscore(score) outcome(re78) kernel k(normal) bw(0.06)
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psmatch2 treated, pscore(score) outcome(re78) kernel k(epan) bw(0.01)
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psmatch2 treated, pscore(score) outcome(re78) kernel k(normal) bw(0.01)
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// Mahalanobis-metric matching
/************************************************************************/
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psmatch2 treated, mahal(age black hispanic married educ nodegree re75) outcome(re78)
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pstest2 age black hispanic married educ nodegree re75, sum graph
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psmatch2 treated, mahal(score age black hispanic married educ nodegree re75) outcome(re78)
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pstest2 age black hispanic married educ nodegree re75, sum graph
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// ATNT
/************************************************************************/
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gen treat0 = 1-treated
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psmatch2 treat0 age black hispanic married educ nodegree re75, outcome(re78)
* Remember to take -1*(effect) for ATNT.
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sum _pdif, d
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psmatch2 treat0 age black hispanic married educ nodegree re75, outcome(re78) cal(0.01)
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tab _weight if treat0==0
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pstest2 age black hispanic married educ nodegree re75, sum graph
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psmatch2 treated age black hispanic married educ nodegree re75, outcome(re78) kernel ate
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film re78 treated age black hispanic married educ nodegree re75, ate
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qui regress re78 treated##c.age treated##black treated##hispanic treated##married treated##c.educ treated##nodegree treated##c.re75
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margins, dydx(treated)
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margins, dydx(treated) over(treated)
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