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Unlike difference in differences approaches, this method can account for the effects of confounders changing over time, by weighting the control group to better match the treatment group before the intervention. Another advantage of the synthetic control method is that it allows researchers to systematically select comparison groups. It has been applied to the fields of political science, health policy, criminology, and economics.

Unlike difference in differences approaches, this method can account for the effects of confounders changing over time, by weighting the control group to better match the treatment group before the intervention. Another advantage of the synthetic control method is that it allows researchers to systematically select comparison groups. It has been applied to the fields of political science, health policy, criminology, and economics.

【翻译】合成对照方法是一种统计方法，用于评估比较案例研究中的干预措施的效果。它使用多组数据加权组合构建对照组，并与治疗组进行比较。这种比较被用来估计如果治疗组没有接受治疗会发生什么。与双重差分（Difference in difference）方法不同，这种方法可以纳入随时间变化的混杂因素的影响，通过调整对照组的加权系数，能更好地对干预前的治疗组数据进行匹配。合成对照的另一个优点是，它允许研究人员在多组候选数据中做系统性选择。它已应用于政治学、卫生政策、犯罪学和经济学等领域。

【翻译】合成对照是一种统计方法，在比较案例研究中用于评估干预措施的效果。它使用多组数据的加权组合来构建对照组，使之与治疗组进行比较。基于这种比较，可用来估计在干预之后的时间里，假如没有对治疗组进行干预的情况下治疗组将如何发展。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。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。它已应用于政治学、卫生政策、犯罪学和经济学等多个领域。

The synthetic control method combines elements from [[Matching (statistics)|matching]] and [[difference-in-differences]] techniques. Difference-in-differences methods are often-used policy evaluation tools that estimate the effect of an intervention at an aggregate level (e.g. state, country, age group etc.) by averaging over a set of unaffected units. Famous examples include studies of the employment effects of a raise in the [[Minimum wage in the United States|minimum wage]] in New Jersey fast food restaurants by comparing them to fast food restaurants just across the border in [[Philadelphia]] that were unaffected by a minimum wage raise,<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> and studies that look at [[crime rates]] in southern cities to evaluate the impact of the [[Mariel boat lift]] on crime.<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>  The control group in this specific scenario can be interpreted as a [[Weighted arithmetic mean|weighted average]], where some units effectively receive zero weight while others get an equal, non-zero weight.

The synthetic control method combines elements from [[Matching (statistics)|matching]] and [[difference-in-differences]] techniques. Difference-in-differences methods are often-used policy evaluation tools that estimate the effect of an intervention at an aggregate level (e.g. state, country, age group etc.) by averaging over a set of unaffected units. Famous examples include studies of the employment effects of a raise in the [[Minimum wage in the United States|minimum wage]] in New Jersey fast food restaurants by comparing them to fast food restaurants just across the border in [[Philadelphia]] that were unaffected by a minimum wage raise,<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> and studies that look at [[crime rates]] in southern cities to evaluate the impact of the [[Mariel boat lift]] on crime.<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>  The control group in this specific scenario can be interpreted as a [[Weighted arithmetic mean|weighted average]], where some units effectively receive zero weight while others get an equal, non-zero weight.

The synthetic control method combines elements from matching and difference-in-differences techniques. Difference-in-differences methods are often-used policy evaluation tools that estimate the effect of an intervention at an aggregate level (e.g. state, country, age group etc.) by averaging over a set of unaffected units. Famous examples include studies of the employment effects of a raise in the minimum wage in New Jersey fast food restaurants by comparing them to fast food restaurants just across the border in Philadelphia that were unaffected by a minimum wage raise, and studies that look at crime rates in southern cities to evaluate the impact of the Mariel boat lift on crime.  The control group in this specific scenario can be interpreted as a weighted average, where some units effectively receive zero weight while others get an equal, non-zero weight.

The synthetic control method combines elements from matching and difference-in-differences techniques. Difference-in-differences methods are often-used policy evaluation tools that estimate the effect of an intervention at an aggregate level (e.g. state, country, age group etc.) by averaging over a set of unaffected units. Famous examples include studies of the employment effects of a raise in the minimum wage in New Jersey fast food restaurants by comparing them to fast food restaurants just across the border in Philadelphia that were unaffected by a minimum wage raise, and studies that look at crime rates in southern cities to evaluate the impact of the Mariel boat lift on crime.  The control group in this specific scenario can be interpreted as a weighted average, where some units effectively receive zero weight while others get an equal, non-zero weight.

综合控制方法结合了匹配技术和差中差技术的要素。差异中的差异法是一种常用的政策评估工具，用于在总体水平上评估干预措施的效果(例如:。州、国家、年龄组别等)平均超过一组未受影响的单位。著名的例子包括新泽西州快餐店提高最低工资对就业影响的研究，比较对象是紧邻州边境的费城，那边的快餐店没有受到提高最低工资的影响，以及研究南部城市的犯罪率来评估马里埃尔移民潮对犯罪率的影响。在这个特定的场景中，控制组可以被解释为一个加权平均数，其中一些单位实际上得到了零重量，而其他单位得到了相等的，非零重量。

综合控制方法结合了匹配技术和差中差技术的要素。差异中的差异法是一种常用的政策评估工具，用于在总体水平上评估干预措施的效果(例如:。州、国家、年龄组别等)平均超过一组未受影响的单位。著名的例子包括新泽西州快餐店提高最低工资对就业影响的研究，比较对象是紧邻州边境的费城，那边的快餐店没有受到提高最低工资的影响，以及研究南部城市的犯罪率来评估马里埃尔船只提升对犯罪率的影响。在这个特定的场景中，控制组可以被解释为一个加权平均数，其中一些单位实际上得到了零重量，而其他单位得到了相等的，非零重量。

The synthetic control method tries to offer a more systematic way to assign weights to the control group. It typically uses a relatively long time series of the outcome prior to the intervention and estimates weights in such a way that the control group mirrors the treatment group as closely as possible. In particular, assume we have ''J'' observations over ''T'' time periods where the relevant treatment occurs at time <math>T_{0}</math> where <math>T_{0}<T.</math> Let  

The synthetic control method tries to offer a more systematic way to assign weights to the control group. It typically uses a relatively long time series of the outcome prior to the intervention and estimates weights in such a way that the control group mirrors the treatment group as closely as possible. In particular, assume we have ''J'' observations over ''T'' time periods where the relevant treatment occurs at time <math>T_{0}</math> where <math>T_{0}<T.</math> Let  

【翻译】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。它通常采用干预之前相对比较长时间段内的多个时间序列，估计一组权值使得对这些时间序列的结果进行加权后得到的控制组时间序列尽可能去拟合治疗组的时间序列。特别地，假设我们在总共T个时间段里有J个观测量（单位），其中一个单位接受了治疗，相应的治疗发生在T_{0}时间段，且T_{0}<T。让

【翻译】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T_{0}时间接受了治疗，T_{0}<T。让

: \alpha_{it}=Y_{it}-Y^N_{it},

: \alpha_{it}=Y_{it}-Y^N_{it},

为单位 i 的在时间 t 的治疗效果，其中 Y^N_{it} 是未经治疗的结果。不失一般性，如果单位1接受了相应的治疗，只有在 t > T_{0} 时段的 Y^N_{1t} 没有被观察到。我们的目标是估计(\alpha_{1T_{0}+1} ... ... \alpha_{1T})。

为单元 i 的在时间 t 的治疗效果，其中 Y^N_{it} 是未经治疗的结果。不失一般性，如果指定单元1接受治疗，则只有单元1的数据 Y^N_{1t}在 t > T_{0} 时段是无法观测的。而我们的目标是要估计(\alpha_{1T_{0}+1} ... ... \alpha_{1T})估。

for t>T_{0}.因此，在一定的正则性条件下，这种权重可以作为利息处理效果的估计量。本质上，该方法采用了匹配的思想，并利用训练数据进行干预前的权重设置，从而得到干预后的相应控制。

for t>T_{0}.因此，在一定的正则性条件下，这种权重可以作为利息处理效果的估计量。本质上，该方法采用了匹配的思想，并利用训练数据进行干预前的权重设置，从而得到干预后的相应控制。

:

:

对于<math>t\leqslant T_{0}</math>，假设存在一些最优权重<math>w_2, \ldots, w_J</math>，使得

对于<math>t\leqslant T_{0}</math>，假设存在一些最优权重<math>w_2, \ldots, w_J</math>，使得

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

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

而对于<math>t>T_{0}</math>，合成对照方法建议使用这些权重来估计反事实

而对于<math>t>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，该方法使用匹配的思想，并利用干预前的训练数据计算权重，进而能够计算干预后的相关控制组数据。<ref name=":0" />  

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" />