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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|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>

合成对照方法试图用一种更加系统的方法为控制组分配权重。它通常用干预之前比较长一段时间内的多个时间序列作为输入数据，估计一组权重值使得这些输入数据加权的结果尽可能地拟合治疗组的时间序列数据，并将结果用作控制组时间序列数据。特别地，假设我们在T个时间段里共有J个观测量（单元），其中一个单元在<math>T_{0}</math>时间接受了治疗，<math>T_{0}<T.</math>。让

合成对照方法试图用一种更加系统的方法为控制组分配权重。它通常用干预之前比较长一段时间内的多个时间序列作为输入数据，估计一组权重值使得这些输入数据加权的结果尽可能地拟合治疗组的时间序列数据，并将结果用作控制组时间序列数据。特别地，假设我们在T个时间段里共有J个观测量（单元），其中一个单元在<math>T_{0}</math>时间接受了治疗，<math>T_{0}<T</math>。让

:<math>\alpha_{it}=Y_{it}-Y^N_{it},</math>

:<math>\alpha_{it}=Y_{it}-Y^N_{it},</math>