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第3行： − 作为一种统计技术，'''匹配 Matching'''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。<ref>{{cite journal | doi=10.2307/2529684 | last=Rubin | first=Donald B. | title=Matching to Remove Bias in Observational Studies | journal=Biometrics | volume=29 | issue=1 | year=1973 | pages=159–183 | jstor=2529684}}</ref><ref>{{cite journal | title=On Stratification, Grouping and Matching | last=Anderson | first=Dallas W. |author2=Kish, Leslie |author3=Cornell, Richard G. | journal=Scandinavian Journal of Statistics | volume=7 | issue=2 | year=1980 | pages=61–66 | jstor=4615774}}</ref><ref>{{cite journal | doi=10.2307/2530417 | title=Matching in Epidemiologic Studies: Validity and Efficiency Considerations | last=Kupper | first=Lawrence L. |author2=Karon, John M. |author3=Kleinbaum, David G. |author4=Morgenstern, Hal |author5= Lewis, Donald K. | journal=Biometrics | volume=37 | issue=2 | year=1981 | pages=271–291 | jstor=2530417 | pmid=7272415| citeseerx=10.1.1.154.1197 }}</ref>'''<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>'''<ref name="Rosenbaum Rubin">{{cite journal |last1=Rosenbaum |first1=Paul R. |last2=Rubin |first2=Donald B. |title=The Central Role of the Propensity Score in Observational Studies for Causal Effects |journal=Biometrika |year=1983 |volume=70 |issue=1 |pages=41–55 |doi=10.1093/biomet/70.1.41 |doi-access=free }}</ref>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。<ref>{{Cite journal|last1=King|first1=Gary|last2=Nielsen|first2=Richard|date=October 2019|title=Why Propensity Scores Should Not Be Used for Matching|url=https://www.cambridge.org/core/product/identifier/S1047198719000111/type/journal_article|journal=Political Analysis|language=en|volume=27|issue=4|pages=435–454|doi=10.1017/pan.2019.11|issn=1047-1987|doi-access=free}}</ref>+ + + − 匹配由'''<font color="#ff8000"> 唐纳德•鲁宾 Donald Rubin </font>'''<ref name="Rosenbaum Rubin" />推动，在经济学中主要受到LaLonde<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>的批评。LaLonde比较了一个实验中的处理效果估计和运用匹配方法产生的可比估计，表明匹配方法是有偏的。Dehejia和Wahba重新评估了LaLonde的批评，并指出匹配是一个很好的解决方案。<ref>{{cite journal | title = Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs |first1 = R. H. |last1=Dehejia |first2 = S. |last2=Wahba |journal=Journal of the American Statistical Association |year=1999 |volume=94 |issue=448 |pages=1053–1062 |doi=10.1080/01621459.1999.10473858 |url = http://www.nber.org/papers/w6586.pdf }}</ref>政治学<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>和社会学期刊<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}}</ref>上也提出了类似的批评。+ − − == 分析 == − 当感兴趣的结果是二元变量时，分析匹配数据最常用的工具是条件Logistic回归模型，因为它可以处理'''<font color="#32cd32"> 任意大小的层次和连续或二元处理变量（自变量）strata of arbitrary size and continuous or binary treatments (predictors)</font>''' ，并且可以控制协变量。在特定情况下，可以使用'''<font color="#32cd32"> 配对差异检验 paired difference test</font>'''、 McNemar 检验和 Cochran-Mantel-Haenzel 检验等更简单的检验。 + − 当感兴趣的结果是连续的，对'''<font color="#ff8000">平均处理效应 Average Treatment Effect </font>'''进行估计。 − + +
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|description='通过在观察研究或准实验研究中比较已处理和未处理的单元，以评估处理的效果
|description='通过在观察研究或准实验研究中比较已处理和未处理的单元，以评估处理的效果
}}
}}
'''匹配(Matching)是'''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，通过比较已接受处理和未接受处理的个体(unit)，以评估处理效应的一种统计技术。匹配的目标是，对每个处理组的个体，找到一个(或多个)具有相似可观察特征的控制组(即未接受处理组)的个体，以评估处理效应。通过将处理组个体与相似的控制组个体进行匹配，匹配后的处理组个体与控制组个体的结果可直接比较，从而可以减少混杂效应带来的偏差，评估处理效应。<ref>{{cite journal | doi=10.2307/2529684 | last=Rubin | first=Donald B. | title=Matching to Remove Bias in Observational Studies | journal=Biometrics | volume=29 | issue=1 | year=1973 | pages=159–183 | jstor=2529684}}</ref><ref>{{cite journal | title=On Stratification, Grouping and Matching | last=Anderson | first=Dallas W. |author2=Kish, Leslie |author3=Cornell, Richard G. | journal=Scandinavian Journal of Statistics | volume=7 | issue=2 | year=1980 | pages=61–66 | jstor=4615774}}</ref><ref>{{cite journal | doi=10.2307/2530417 | title=Matching in Epidemiologic Studies: Validity and Efficiency Considerations | last=Kupper | first=Lawrence L. |author2=Karon, John M. |author3=Kleinbaum, David G. |author4=Morgenstern, Hal |author5= Lewis, Donald K. | journal=Biometrics | volume=37 | issue=2 | year=1981 | pages=271–291 | jstor=2530417 | pmid=7272415| citeseerx=10.1.1.154.1197 }}</ref>'''<font color="#ff8000"> 倾向性得分匹配(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>''')'''<font color="#ff8000"><ref name="Rosenbaum Rubin">{{cite journal |last1=Rosenbaum |first1=Paul R. |last2=Rubin |first2=Donald B. |title=The Central Role of the Propensity Score in Observational Studies for Causal Effects |journal=Biometrika |year=1983 |volume=70 |issue=1 |pages=41–55 |doi=10.1093/biomet/70.1.41 |doi-access=free }}</ref></font>'''的一部分发展起来的。它已被证明会增加模型依赖性、偏差、降低估计的效率和检验的功效('''<font color="#32cd32">power) </font>'''，与其他匹配方法相比不再推荐使用。<ref>{{Cite journal|last1=King|first1=Gary|last2=Nielsen|first2=Richard|date=October 2019|title=Why Propensity Scores Should Not Be Used for Matching|url=https://www.cambridge.org/core/product/identifier/S1047198719000111/type/journal_article|journal=Political Analysis|language=en|volume=27|issue=4|pages=435–454|doi=10.1017/pan.2019.11|issn=1047-1987|doi-access=free}}</ref>
匹配由'''<font color="#ff8000">唐纳德•鲁宾(Donald Rubin)</font>'''<ref name="Rosenbaum Rubin" />推动，在经济学中主要受到LaLonde<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>的批评。LaLonde在一个实验中比较了处理效应估计和运用匹配方法产生的可比估计，表明匹配方法是有偏的。Dehejia和Wahba重新评估了LaLonde的批评，并指出匹配是一个很好的解决方案。<ref>{{cite journal | title = Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs |first1 = R. H. |last1=Dehejia |first2 = S. |last2=Wahba |journal=Journal of the American Statistical Association |year=1999 |volume=94 |issue=448 |pages=1053–1062 |doi=10.1080/01621459.1999.10473858 |url = http://www.nber.org/papers/w6586.pdf }}</ref>政治学<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>和社会学期刊<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}}</ref>上也提出了类似的批评。
== 分析 ==
当感兴趣的结果是二元变量时，分析匹配数据最常用的工具是条件Logistic回归模型，因为它可以处理'''<font color="#32cd32">任意大小的层次和连续或二元处理变量（自变量）strata of arbitrary size and continuous or binary treatments (predictors)</font>''' ，并且可以控制协变量。在特定情况下，可以使用'''<font color="#32cd32"> 配对差异检验 paired difference test</font>'''、 McNemar 检验和 Cochran-Mantel-Haenzel 检验等更简单的检验。
当感兴趣的结果是连续的，对'''<font color="#ff8000">平均处理效应 Average Treatment Effect </font>'''进行估计。
匹配也可用于在通过其他技术分析之前（例如回归分析）“预处理”样本。<ref>{{cite journal |last1=Ho |first1=Daniel E. |first2=Kosuke |last2=Imai |first3=Gary |last3=King |first4=Elizabeth A. |last4=Stuart |year=2007 |title=Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference |journal=Political Analysis |volume=15 |issue=3 |pages=199–236 |doi=10.1093/pan/mpl013 |doi-access=free }}</ref>
匹配也可用于在通过其他技术分析之前（例如回归分析）“预处理”样本。<ref>{{cite journal |last1=Ho |first1=Daniel E. |first2=Kosuke |last2=Imai |first3=Gary |last3=King |first4=Elizabeth A. |last4=Stuart |year=2007 |title=Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference |journal=Political Analysis |volume=15 |issue=3 |pages=199–236 |doi=10.1093/pan/mpl013 |doi-access=free }}</ref>
== 过匹配 ==
== 过匹配 ==
过匹配是对表面是中介变量、实际上是暴露的结果进行匹配。如果中介变量本身是分层的，则很可能引致一种暴露与疾病的令人费解的关系。<ref name=marsh/>过匹配因此导致统计偏误。<ref name=marsh>{{cite journal |title=Removal of radiation dose response effects: an example of over-matching |last1=Marsh |first1=J. L. |last2=Hutton |first2=J. L. |last3=Binks |first3=K. |year=2002 |journal=British Medical Journal |volume=325 |issue=7359 |pages=327–330 |pmid=12169512 |doi=10.1136/bmj.325.7359.327 |pmc=1123834}}</ref>
过匹配是对表面是中介变量、实际上是暴露的结果进行匹配。如果中介变量本身是分层的，则很可能引致一种暴露与疾病的令人费解的关系。<ref name=marsh/>过匹配因此导致统计偏误。<ref name=marsh>{{cite journal |title=Removal of radiation dose response effects: an example of over-matching |last1=Marsh |first1=J. L. |last2=Hutton |first2=J. L. |last3=Binks |first3=K. |year=2002 |journal=British Medical Journal |volume=325 |issue=7359 |pages=327–330 |pmid=12169512 |doi=10.1136/bmj.325.7359.327 |pmc=1123834}}</ref>
例如，在估计体外受精（IVF）后的围产期死亡率和出生体重时，按妊娠期和/或多胎数来匹配对照组就是过度匹配，因为IVF本身会增加早产和多胎的风险。<ref>{{cite journal |title=The danger of overmatching in studies of the perinatal mortality and birthweight of infants born after assisted conception |last1=Gissler |first1=M. |last2=Hemminki |first2=E. |journal=Eur J Obstet Gynecol Reprod Biol |year=1996 |volume=69 |issue=2 |pages=73–75 |pmid=8902436 |doi=10.1016/0301-2115(95)02517-0}}</ref>
例如，在估计体外受精（IVF）后的围产期死亡率和出生体重时，按妊娠期和/或多胎数来匹配对照组就是过度匹配，因为IVF本身会增加早产和多胎的风险。<ref>{{cite journal |title=The danger of overmatching in studies of the perinatal mortality and birthweight of infants born after assisted conception |last1=Gissler |first1=M. |last2=Hemminki |first2=E. |journal=Eur J Obstet Gynecol Reprod Biol |year=1996 |volume=69 |issue=2 |pages=73–75 |pmid=8902436 |doi=10.1016/0301-2115(95)02517-0}}</ref>