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第13行： − 匹配是由唐纳德•鲁宾(Donald Rubin)推动的。'''<font color="#ff8000"> 拉隆德 LaLonde</font>'''（1986）在经济学中对其提出了他们将实验中的治疗效果评估与匹配方法产生的可比评估进行了比较，并表明匹配方法是有偏差的。德赫加和瓦巴(1999)重新评价了拉隆德的批评，并指出匹配是一个很好的解决方案。类似的批评在政治学和社会学期刊上也被提出。 第22行：
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Matching has been promoted by Donald Rubin. who compared estimates of treatment effects from an experiment to comparable estimates produced with matching methods and showed that matching methods are biased. Dehejia and Wahba (1999) reevaluated LaLonde's critique and showed that matching is a good solution. Similar critiques have been raised in political science and sociology journals.
Matching has been promoted by Donald Rubin. who compared estimates of treatment effects from an experiment to comparable estimates produced with matching methods and showed that matching methods are biased. Dehejia and Wahba (1999) reevaluated LaLonde's critique and showed that matching is a good solution. Similar critiques have been raised in political science and sociology journals.
{{cite journal | last = LaLonde | first = Robert J. | title = Evaluating the Econometric Evaluations of Training Programs with Experimental Data | journal = [[American Economic Review]] | volume = 76 | issue = 4 |year = 1986 | pages = 604–620 | jstor=1806062 }}</ref> who compared estimates of treatment effects from an [[experiment]] to comparable estimates produced with matching methods and showed that matching methods are [[Bias (statistics)|biased]]. Dehejia and Wahba (1999) reevaluated LaLonde's critique and showed that matching is a good solution.<ref>
{{cite journal | last = LaLonde | first = Robert J. | title = Evaluating the Econometric Evaluations of Training Programs with Experimental Data | journal = [[American Economic Review]] | volume = 76 | issue = 4 |year = 1986 | pages = 604–620 | jstor=1806062 }}</ref> who compared estimates of treatment effects from an [[experiment]] to comparable estimates produced with matching methods and showed that matching methods are [[Bias (statistics)|biased]]. Dehejia and Wahba (1999) reevaluated LaLonde's critique and showed that matching is a good solution.<ref>
url = http://www.nber.org/papers/w6586.pdf }}</ref> Similar critiques have been raised in [[political science]]<ref>{{cite journal |last1=Arceneaux |first1=Kevin |first2=Alan S. |last2=Gerber |first3=Donald P. |last3=Green |year=2006 |title=Comparing Experimental and Matching Methods Using a Large-Scale Field Experiment on Voter Mobilization |journal=Political Analysis |volume=14 |issue=1 |pages=37–62 |doi=10.1093/pan/mpj001 }}</ref> and [[sociology]]<ref>{{cite journal |last1=Arceneaux |first1=Kevin |first2=Alan S. |last2=Gerber |first3=Donald P. |last3=Green |year=2010 |title=A Cautionary Note on the Use of Matching to Estimate Causal Effects: An Empirical Example Comparing Matching Estimates to an Experimental Benchmark |journal=Sociological Methods & Research |volume=39 |issue=2 |pages=256–282 |doi=10.1177/0049124110378098 |s2cid=37012563 }}</ref> journals.
url = http://www.nber.org/papers/w6586.pdf }}</ref> Similar critiques have been raised in [[political science]]<ref>{{cite journal |last1=Arceneaux |first1=Kevin |first2=Alan S. |last2=Gerber |first3=Donald P. |last3=Green |year=2006 |title=Comparing Experimental and Matching Methods Using a Large-Scale Field Experiment on Voter Mobilization |journal=Political Analysis |volume=14 |issue=1 |pages=37–62 |doi=10.1093/pan/mpj001 }}</ref> and [[sociology]]<ref>{{cite journal |last1=Arceneaux |first1=Kevin |first2=Alan S. |last2=Gerber |first3=Donald P. |last3=Green |year=2010 |title=A Cautionary Note on the Use of Matching to Estimate Causal Effects: An Empirical Example Comparing Matching Estimates to an Experimental Benchmark |journal=Sociological Methods & Research |volume=39 |issue=2 |pages=256–282 |doi=10.1177/0049124110378098 |s2cid=37012563 }}</ref> journals.
匹配是由唐纳德•鲁宾(Donald Rubin)推动的。'''<font color="#ff8000"> 拉隆德 LaLonde</font>'''（1986）在经济学中对其提出了他们将实验中的治疗效果评估与匹配方法产生的可比评估进行了比较，并表明匹配方法是有偏差的。德赫加和瓦巴(1999)重新评价了拉隆德的批评，并指出匹配是一个很好的解决方案。类似的批评在政治学和社会学期刊上也被提出。
When the outcome of interest is binary, the most general tool for the analysis of matched data is conditional logistic regression as it handles strata of arbitrary size and continuous or binary treatments (predictors) and can control for covariates. In particular cases, simpler tests like paired difference test, McNemar test and Cochran-Mantel-Haenszel test are available.
When the outcome of interest is binary, the most general tool for the analysis of matched data is conditional logistic regression as it handles strata of arbitrary size and continuous or binary treatments (predictors) and can control for covariates. In particular cases, simpler tests like paired difference test, McNemar test and Cochran-Mantel-Haenszel test are available.