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与'''双重差分方法 Difference in difference'''不同，这种方法可以考虑混杂因素随时间变化的影响，通过调整对照组的加权组合，可以对干预之前的治疗组数据做更好的匹配。<ref name=he>{{cite journal|last1=Kreif|first1=Noémi|last2=Grieve|first2=Richard|last3=Hangartner|first3=Dominik|last4=Turner|first4=Alex James|last5=Nikolova|first5=Silviya|last6=Sutton|first6=Matt|title=Examination of the Synthetic Control Method for Evaluating Health Policies with Multiple Treated Units|journal=Health Economics|date=December 2016|volume=25|issue=12|pages=1514–1528|doi=10.1002/hec.3258|pmid=26443693|pmc=5111584}}</ref>合成对照还有个优点是，它允许研究人员在多组候选数据中做系统性选择。<ref name=ajps>{{cite journal|last1=Abadie|first1=Alberto|authorlink1=Alberto Abadie|last2=Diamond|first2=Alexis|last3=Hainmueller|first3=Jens|title=Comparative Politics and the Synthetic Control Method|journal=American Journal of Political Science|date=February 2015|volume=59|issue=2|pages=495–510|doi=10.1111/ajps.12116}}</ref>它已应用于政治学<ref name=ajps/>、卫生政策<ref name=he/>、犯罪学<ref>{{cite journal|last1=Saunders|first1=Jessica|last2=Lundberg|first2=Russell|last3=Braga|first3=Anthony A.|last4=Ridgeway|first4=Greg|last5=Miles|first5=Jeremy|title=A Synthetic Control Approach to Evaluating Place-Based Crime Interventions|journal=Journal of Quantitative Criminology|date=3 June 2014|volume=31|issue=3|pages=413–434|doi=10.1007/s10940-014-9226-5}}</ref>和经济学等多个领域。<ref>{{cite journal|last1=Billmeier|first1=Andreas|last2=Nannicini|first2=Tommaso|title=Assessing Economic Liberalization Episodes: A Synthetic Control Approach|journal=Review of Economics and Statistics|date=July 2013|volume=95|issue=3|pages=983–1001|doi=10.1162/REST_a_00324}}</ref>

与'''双重差分方法 Difference in difference'''不同，这种方法可以考虑混杂因素随时间变化的影响，通过调整对照组的加权组合，可以对干预之前的治疗组数据做更好的匹配。<ref name=he>{{cite journal|last1=Kreif|first1=Noémi|last2=Grieve|first2=Richard|last3=Hangartner|first3=Dominik|last4=Turner|first4=Alex James|last5=Nikolova|first5=Silviya|last6=Sutton|first6=Matt|title=Examination of the Synthetic Control Method for Evaluating Health Policies with Multiple Treated Units|journal=Health Economics|date=December 2016|volume=25|issue=12|pages=1514–1528|doi=10.1002/hec.3258|pmid=26443693|pmc=5111584}}</ref>合成对照还有个优点是，它允许研究人员在多组候选数据中做系统性选择。<ref name=ajps>{{cite journal|last1=Abadie|first1=Alberto|last2=Diamond|first2=Alexis|last3=Hainmueller|first3=Jens|title=Comparative Politics and the Synthetic Control Method|journal=American Journal of Political Science|date=February 2015|volume=59|issue=2|pages=495–510|doi=10.1111/ajps.12116}}</ref>它已应用于政治学<ref name=ajps/>、卫生政策<ref name=he/>、犯罪学<ref>{{cite journal|last1=Saunders|first1=Jessica|last2=Lundberg|first2=Russell|last3=Braga|first3=Anthony A.|last4=Ridgeway|first4=Greg|last5=Miles|first5=Jeremy|title=A Synthetic Control Approach to Evaluating Place-Based Crime Interventions|journal=Journal of Quantitative Criminology|date=3 June 2014|volume=31|issue=3|pages=413–434|doi=10.1007/s10940-014-9226-5}}</ref>和经济学等多个领域。<ref>{{cite journal|last1=Billmeier|first1=Andreas|last2=Nannicini|first2=Tommaso|title=Assessing Economic Liberalization Episodes: A Synthetic Control Approach|journal=Review of Economics and Statistics|date=July 2013|volume=95|issue=3|pages=983–1001|doi=10.1162/REST_a_00324}}</ref>

合成对照方法结合了匹配方法和双重差分方法的技术要素。双重差分法也是一种常用的政策评估工具，通过比较被干预单元和未被干预单元在总体水平上（例如：州、国家、年龄组别等）的均值差异来评估政策干预效果。著名的案例包括新泽西州快餐店提高最低工资政策对就业影响的研究，<ref name="CardKrueger">{{cite journal |last=Card |first=D. |authorlink=David Card |first2=A. |last2=Krueger |authorlink2=Alan Krueger |year=1994 |title=Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania |journal=[[American Economic Review]] |volume=84 |issue=4 |pages=772–793 |jstor=2118030 }}</ref>比较对象是在新泽西州边界另一侧，费城那边那些没受到该政策影响的快餐店；还有通过研究南部城市的犯罪率来评估马里埃尔移民潮如何影响犯罪的案例。<ref>{{cite journal |last=Card |first=D. |year=1990 |title=The Impact of the Mariel Boatlift on the Miami Labor Market |journal=[[Industrial and Labor Relations Review]] |volume=43 |issue=2 |pages=245–257 |doi=10.1177/001979399004300205 |url=http://arks.princeton.edu/ark:/88435/dsp016h440s46f }}</ref>在双重差分场景中，合成对照的控制组可被理解为一个加权平均，其中的一些单元相当于得到了零权重，而另外的一些单元则得到了非零权重（每个单元内的数据共享同一权重值）。

合成对照方法结合了匹配方法和双重差分方法的技术要素。双重差分法也是一种常用的政策评估工具，通过比较被干预单元和未被干预单元在总体水平上（例如：州、国家、年龄组别等）的均值差异来评估政策干预效果。著名的案例包括新泽西州快餐店提高最低工资政策对就业影响的研究，<ref name="CardKrueger">{{cite journal |last=Card |first=D. |last2=Krueger |year=1994 |title=Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania |journal=[[American Economic Review]] |volume=84 |issue=4 |pages=772–793 |jstor=2118030 }}</ref>比较对象是在新泽西州边界另一侧，费城那边那些没受到该政策影响的快餐店；还有通过研究南部城市的犯罪率来评估马里埃尔移民潮如何影响犯罪的案例。<ref>{{cite journal |last=Card |first=D. |year=1990 |title=The Impact of the Mariel Boatlift on the Miami Labor Market |journal=Industrial and Labor Relations Review |volume=43 |issue=2 |pages=245–257 |doi=10.1177/001979399004300205 |url=http://arks.princeton.edu/ark:/88435/dsp016h440s46f }}</ref>在双重差分场景中，合成对照的控制组可被理解为一个加权平均，其中的一些单元相当于得到了零权重，而另外的一些单元则得到了非零权重（每个单元内的数据共享同一权重值）。

而对于<math>t\leqslant T_{0}</math>，合成对照方法建议使用这些权重来做出反事实估计

而对于<math>t\leqslant T_{0}</math>，合成对照方法建议使用这些权重来做出反事实估计

:<math>Y^N_{1t}=\Sigma^J_{j=2}w_{j}Y_{jt}</math>  

:<math>Y^N_{1t}=\Sigma^J_{j=2}w_{j}Y_{jt}</math>  

因此，在一定的正则性条件下，此类权重可以作为我们所关心的治疗效果的估计量。本质上，该方法基于匹配的思想，利用干预前的数据训练得到加权组合的控制组，进而可以对干预后的控制组数据进行推断。<ref name=":0">{{cite journal |last=Abadie |first=A. |authorlink=Alberto Abadie |first2=A. |last2=Diamond |first3= J. |last3=Hainmüller |year=2010 |title=Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program |journal=[[Journal of the American Statistical Association]] |volume=105 |issue=490 |pages=493–505 |doi=10.1198/jasa.2009.ap08746 }}</ref>

因此，在一定的正则性条件下，此类权重可以作为我们所关心的治疗效果的估计量。本质上，该方法基于匹配的思想，利用干预前的数据训练得到加权组合的控制组，进而可以对干预后的控制组数据进行推断。<ref name=":0">{{cite journal |last=Abadie |first=A. |first2=A. |last2=Diamond |first3= J. |last3=Hainmüller |year=2010 |title=Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program |journal=[[Journal of the American Statistical Association]] |volume=105 |issue=490 |pages=493–505 |doi=10.1198/jasa.2009.ap08746 }}</ref>