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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。

匹配是由'''<font color="#ff8000"> 唐纳德•鲁宾 Donald Rubin </font>'''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.