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| | + | |keywords=结构因果模型,因果科学,因果之梯 |
| | + | |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 |
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| | + | 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。<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> |
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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]].<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> [[Propensity score matching]], an early matching technique, was developed as part of the [[Rubin causal model]],<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> but has been shown to increase model dependence, bias, inefficiency, and power and is no longer recommended compared to other matching methods.<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>
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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.
| + | 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'''<font color="#ff8000"> 拉隆德 LaLonde</font>'''（1986）<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。'''<font color="#ff8000"> 德赫加和瓦巴 Dehejia and Wahba </font>'''(1999)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 |s2cid=37012563 }}</ref>上也提出了类似的批评。 |
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| − | 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。
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| | + | == 分析 == |
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| | + | 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。 |
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| − | Matching has been promoted by [[Donald Rubin]].<ref name="Rosenbaum Rubin" /> It was prominently criticized in [[economics]] by LaLonde (1986),<ref>
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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.
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| − | {{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>
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| − | {{cite journal | title = Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs |
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| − | 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 |
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| − | 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.
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| − | 匹配由'''<font color="#ff8000"> 唐纳德•鲁宾 Donald Rubin </font>'''推动，在经济学中主要受到'''<font color="#ff8000"> 拉隆德 LaLonde</font>'''（1986）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。'''<font color="#ff8000"> 德赫加和瓦巴 Dehejia and Wahba </font>'''(1999)重新评估了LaLonde的批评，并指出匹配是一个很好的解决方案。政治学和社会学期刊上也提出了类似的批评。
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| − | == Analysis ==
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| − | 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.
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| − | 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.
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| − | 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。
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| − | When the outcome of interest is continuous, estimation of the average treatment effect is performed.
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| − | When the outcome of interest is continuous, estimation of the [[average treatment effect]] is performed.
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| | 当感兴趣的结果是连续的，对 '''<font color="#ff8000"> 平均处理效 Average Treatment Effect </font>'''应进行估计。 | | 当感兴趣的结果是连续的，对 '''<font color="#ff8000"> 平均处理效 Average Treatment Effect </font>'''应进行估计。 |
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| | + | 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> |
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| − | Matching can also be used to "pre-process" a sample before analysis via another technique, such as [[regression analysis]].<ref>{{cite journal |last1=Ho |first1=Daniel E. |first2=Kosuke |last2=Imai |first3=Gary |last3=King |first4=Elizabeth A. |last4=Stuart |author4-link= Elizabeth A. 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>
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| | + | == 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> |
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| − | Matching can also be used to "pre-process" a sample before analysis via another technique, such as regression analysis.
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| − | 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。
| + | 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> |
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| − | == Overmatching ==
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| − | ''Overmatching'' is matching for an apparent mediator that actually is a result of the exposure. If the mediator itself is stratified, an obscured relation of the exposure to the disease would highly be likely to be induced.<ref name=marsh/> Overmatching thus causes [[statistical bias]].<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. | author2-link = Jane Hutton |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>
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| − | Overmatching is matching for an apparent mediator that actually is a result of the exposure. If the mediator itself is stratified, an obscured relation of the exposure to the disease would highly be likely to be induced.
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| − | 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。过匹配因此导致统计偏误。
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| − | For example, matching the control group by gestation length and/or the number of [[multiple birth]]s when estimating [[perinatal mortality]] and birthweight after [[in vitro fertilization]] (IVF) is overmatching, since IVF itself increases the risk of premature birth and multiple birth.<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>
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| − | For example, matching the control group by gestation length and/or the number of multiple births when estimating perinatal mortality and birthweight after in vitro fertilization (IVF) is overmatching, since IVF itself increases the risk of premature birth and multiple birth.
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| − | 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。
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| − | It may be regarded as a [[sampling bias]] in decreasing the [[external validity]] of a study, because the controls become more similar to the cases in regard to exposure than the general population.
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| − | It may be regarded as a sampling bias in decreasing the external validity of a study, because the controls become more similar to the cases in regard to exposure than the general population.
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| | 它可以被看作是一个降低研究外部效度的抽样偏误，因为相比一般人群，对照组在暴露方面变得更类似于病例。 | | 它可以被看作是一个降低研究外部效度的抽样偏误，因为相比一般人群，对照组在暴露方面变得更类似于病例。 |
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| − | == See also ==
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| − | {{Portal|Mathematics|Medicine}}
| + | == 另见 == |
| | + | * [[倾向得分匹配]] |
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| − | * [[Propensity score matching]]
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| − | | + | ==参考文献 == |
| − | == References == | |
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| | {{reflist|30em}} | | {{reflist|30em}} |
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| − | == Further reading == | + | == 进一步阅读 == |
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| | *{{cite book |last1=Angrist |first1=Joshua D. |last2=Pischke |first2=Jörn-Steffen |chapter=Regression Meets Matching |title=Mostly Harmless Econometrics: An Empiricist's Companion |publisher=Princeton University Press |year=2009 |isbn=978-0-691-12034-8 |pages=69–80 }} | | *{{cite book |last1=Angrist |first1=Joshua D. |last2=Pischke |first2=Jörn-Steffen |chapter=Regression Meets Matching |title=Mostly Harmless Econometrics: An Empiricist's Companion |publisher=Princeton University Press |year=2009 |isbn=978-0-691-12034-8 |pages=69–80 }} |
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| − | 类别: 抽样技术
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| + | '''本词条内容源自wikipedia及公开资料，遵守 CC3.0协议。''' |
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| − | <small>This page was moved from [[wikipedia:en:Matching (statistics)]]. Its edit history can be viewed at [[匹配/edithistory]]</small></noinclude>
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| − | [[Category:待整理页面]] | + | [[Category: 偏误]] |
| | + | [[Category: 实验设计]] |
| | + | [[Category:医学统计]] |
| | + | [[Category: 观察性研究]] |
| | + | [[Category:抽样技术]] |