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Matching is a statistical technique which is used to evaluate the effect of a treatment by comparing the treated and the non-treated units in an observational study or quasi-experiment (i.e. when the treatment is not randomly assigned). The goal of matching is, for every treated unit, to find one (or more) non-treated unit(s) with similar observable characteristics against whom the effect of the treatment can be assessed. By matching treated units to similar non-treated units, matching enables a comparison of outcomes among treated and non-treated units to estimate the effect of the treatment reducing bias due to confounding. Propensity score matching, an early matching technique, was developed as part of the Rubin causal model, but has been shown to increase model dependence, bias, inefficiency, and power and is no longer recommended compared to other matching methods.
Matching is a statistical technique which is used to evaluate the effect of a treatment by comparing the treated and the non-treated units in an observational study or quasi-experiment (i.e. when the treatment is not randomly assigned). The goal of matching is, for every treated unit, to find one (or more) non-treated unit(s) with similar observable characteristics against whom the effect of the treatment can be assessed. By matching treated units to similar non-treated units, matching enables a comparison of outcomes among treated and non-treated units to estimate the effect of the treatment reducing bias due to confounding. Propensity score matching, an early matching technique, was developed as part of the Rubin causal model, but has been shown to increase model dependence, bias, inefficiency, and power and is no longer recommended compared to other matching methods.
作为一种统计技术，'''<font color="#ff8000"> 匹配 Matching</font>'''通过在'''<font color="#ff8000"> 观察研究 Observational Study</font>'''或'''<font color="#ff8000"> 准实验研究 Quasi-experiment</font>'''（即'''<font color="#ff8000"> 处理 Treatment </font>'''是非随机分配的）中比较已处理和未处理的单元，以评估处理的效果。匹配的目标是，对于每个处理单元，找到一个(或多个)具有相似可观察特征的未处理单元，以评估处理效果。通过处理单元与相似未处理单元的匹配，匹配技术可以比较处理单元与未处理单元的不同结果，从而评估处理效应，减少混杂效应带来的偏差。'''<font color="#ff8000"> 倾向值匹配 Propensity Score Matching</font>'''，一种早期的匹配技术，是作为'''<font color="#ff8000"> 鲁宾因果模型 Rubin Causal Model</font>'''的一部分发展起来的，但已被证明会增加模型依赖性、偏差、无效性和'''<font color="#32cd32"> 计算量power </font>'''，与其他匹配方法相比不再被推荐使用。
作为一种统计技术，'''<font color="#ff8000"> 匹配 Matching</font>'''通过在'''<font color="#ff8000"> 观察研究 Observational Study</font>'''或'''<font color="#ff8000"> 准实验研究 Quasi-experiment</font>'''（即'''<font color="#ff8000"> 处理 Treatment </font>'''是非随机分配的）中比较已处理和未处理的单元，以评估处理的效果。匹配的目标是，对于每个处理单元，找到一个(或多个)具有相似可观察特征的未处理单元，以评估处理效果。通过处理单元与相似未处理单元的匹配，匹配技术可以比较处理单元与未处理单元的不同结果，从而评估处理效应，减少混杂效应带来的偏差。'''<font color="#ff8000"> 倾向值匹配 Propensity Score Matching</font>'''，一种早期的匹配技术，是作为'''<font color="#ff8000"> 鲁宾因果模型 Rubin Causal Model</font>'''的一部分发展起来的，但已被证明会增加模型依赖性、偏差、无效性和'''<font color="#32cd32"> 计算量power </font>'''，与其他匹配方法相比不再推荐使用。
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.
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。
{{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>