更改

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>'''早期的匹配技术，是作为虚拟事实模型匹配技术的一部分而发展起来的，但已经被证明会增加模型依赖性、偏倚、低效和功率，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，但已经被证明会增加模型依赖性、偏倚、低效和功率，与其他匹配方法相比不再被推荐使用。