Synthetic Difference In Differences

Arkhangelsky, Dmitry; Athey, Susan; Hirshberg, David A.; Imbens, Guido W.; Wager, Stefan

doi:10.3386/w25532

Cited by 138 publications

(171 citation statements)

References 53 publications

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“…Amjad et al (2018) propose a procedure based on de-noising the outcomes and imputing the missing values. Arkhangelsky et al (2019) propose a new synthetic control as a weighted regression estimator with time fixed effects. Mellace and Pasquini (2019) show how to use SCM to estimate how much of the total effect of intervention goes though observed intermediate outcomes (causal channels).…”

Section: Literature Reviewmentioning

confidence: 99%

The Inclusive Synthetic Control Method

Stefano

Mellace

2020

SSRN Journal

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The Synthetic Control Method (SCM) estimates the causal effect of a policy intervention in a panel data setting with only a few treated units and control units. The treated outcome in the absence of the intervention is recovered by a weighted average of the control units. The latter cannot be affected by the intervention, neither directly nor indirectly. We introduce the inclusive synthetic control method (iSCM), a novel and intuitive synthetic control modification that allows including units potentially affected directly or indirectly by an intervention in the donor pool. Our method is well suited for applications with multiple treated units where including treated units in the donor pool substantially improves the pre-intervention fit and/or for applications where some of the units in the donor pool might be affected by spillover effects. Our iSCM is very easy to implement, and any synthetic control type estimation and inference procedure can be used. Finally, as an illustrative empirical example, we re-estimate the causal effect of German reunification on GDP per capita allowing for spillover effects from West Germany to Austria.

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Section: Literature Reviewmentioning

confidence: 99%

The Inclusive Synthetic Control Method

Stefano

Mellace

2020

SSRN Journal

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“…Specifically, we use SCM to obtain a counterfactual for the LA firearm-related crime rate based on a weighted combination of control units in a donor pool of cities. We match on each year of the pre-intervention period, which has become common in the literature [10,[21][22][23], see], and then we calculate our proposed balance metrics for each matching factor (here, the preintervention outcomes).…”

Section: La Firearm Letter Studymentioning

confidence: 99%

When is a Match Sufficient? A Score-based Balance Metric for the Synthetic Control Method

Parast

Hunt

Griffin

et al. 2020

Journal of Causal Inference

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In some applications, researchers using the synthetic control method (SCM) to evaluate the effect of a policy may struggle to determine whether they have identified a “good match” between the control group and treated group. In this paper, we demonstrate the utility of the mean and maximum Absolute Standardized Mean Difference (ASMD) as a test of balance between a synthetic control unit and treated unit, and provide guidance on what constitutes a poor fit when using a synthetic control. We explore and compare other potential metrics using a simulation study. We provide an application of our proposed balance metric to the 2013 Los Angeles (LA) Firearm Study [9]. Using Uniform Crime Report data, we apply the SCM to obtain a counterfactual for the LA firearm-related crime rate based on a weighted combination of control units in a donor pool of cities. We use this counterfactual to estimate the effect of the LA Firearm Study intervention and explore the impact of changing the donor pool and pre-intervention duration period on resulting matches and estimated effects. We demonstrate how decision-making about the quality of a synthetic control can be improved by using ASMD. The mean and max ASMD clearly differentiate between poor matches and good matches. Researchers need better guidance on what is a meaningful imbalance between synthetic control and treated groups. In addition to the use of gap plots, the proposed balance metric can provide an objective way of determining fit.

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“…To correct for imperfect pre-treatment balance in partially pooled SCM, we augment the partially pooled SCM estimator with a fixed effects outcome model and estimate weighted event studies. Synthetic controls are constructed based on the balance of residualized pre-treatment outcomes; in this way, the approach builds upon recent research on doubly-robust estimators with an extension to the staggered adoption setting [2,5,6,10,20].…”

Section: Weighted Event Studiesmentioning

confidence: 99%

Are We #StayingHome to Flatten the Curve?

Sears

Villas-Boas

et al. 2020

SSRN Journal

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The recent spread of COVID-19 across the U.S. led to concerted efforts by states to "flatten the curve" through the adoption of stay-at-home mandates that encourage individuals to reduce travel and maintain social distance. Combining data on changes in travel activity with COVID-19 health outcomes and state policy adoption, we characterize nationwide changes in mobility patterns, isolate the portion attributable to statewide mandates, and link these reductions to changes in COVID-19 health outcomes. We find evidence of dramatic nationwide declines participants in the National Association for Business Economics Webinar for comments and suggestions. We thank Unacast for providing us access to the data on mobility measures. in mobility prior to any statewide mandates, beginning early in a state's outbreak.Once states adopt a mandate, we estimate further mandate-induced declines between 2.8 and 6.5 percentage points across methods. Using previous changes in mobility, we find significant effects on current mortality and morbidity, with 1% reductions in visits to non-essential businesses weeks prior being associated with 9.2 fewer deaths per 100 million per day, corresponding with over 74,000 lives saved nationwide for the months of March and April -nearly 1.3 times the actual deaths during these months. These averted deaths correspond with estimated economic benefits between $249-$745 billion for observed behavioral changes in March and April. These estimates represent a lower bound of direct health benefits, as they do not account for spillovers or undercounting of COVID-19 mortality. Our findings indicate that statewide policies reduced travel and helped attenuate the negative consequences of COVID-19. Further, substantial reductions in mobility prior to state-level policies convey important policy implications for re-opening. stay-at-home mandates is critical for understanding the benefit of making the considerable economic sacrifices required to enact such policies. Even before mandates limited economic activity, GDP forecasts suggested an economic contraction in the U.S. of 24% [42]. Concerns over these costs prompted comments from the executive branch regarding relaxation of restrictions and allowing non-essential businesses to reopen [52], prompting opposition from public health experts [31,43] and economists [13,36].Recent simulations provide further insight into the benefits of social distancing.While epidemiological models of the U.K. and U.S. suggest that techniques for mitigating exposure of those most at risk may drastically reduce peak load on the healthcare system and cut COVID-19 deaths by half, such techniques on their own might not be enough to prevent the healthcare system from being overwhelmed. Some argue that, in this case, a combination of social distancing, self-quarantine of infected people, and suspension of schools would need to be maintained until a vaccine is available to prevent a rebound [28]. Other experts call for widespread testing coupled with digital contact tracing as a means to re...

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Synthetic Difference In Differences

Cited by 138 publications

References 53 publications

The Inclusive Synthetic Control Method

The Inclusive Synthetic Control Method

When is a Match Sufficient? A Score-based Balance Metric for the Synthetic Control Method

Are We #StayingHome to Flatten the Curve?

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