
@article{xu_generalized_2017,
	title = {Generalized {Synthetic} {Control} {Method}: {Causal} {Inference} with {Interactive} {Fixed} {Effects} {Models}},
	volume = {25},
	issn = {1047-1987, 1476-4989},
	shorttitle = {Generalized {Synthetic} {Control} {Method}},
	url = {https://www.cambridge.org/core/product/identifier/S1047198716000024/type/journal_article},
	doi = {10.1017/pan.2016.2},
	abstract = {Di erence-in-di erences (DID) is commonly used for causal inference in time-series cross-sectional data. It requires the assumption that the average outcomes of treated and control units would have followed parallel paths in the absence of treatment. In this paper, we propose a method that not only relaxes this o en-violated assumption, but also unifies the synthetic control method (Abadie, Diamond, and Hainmueller ) with linear fixed e ects models under a simple framework, of which DID is a special case. It imputes counterfactuals for each treated unit using control group information based on a linear interactive fixed e ects model that incorporates unit-specific intercepts interacted with time-varying coe icients. This method has several advantages. First, it allows the treatment to be correlated with unobserved unit and time heterogeneities under reasonable modeling assumptions. Second, it generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves e iciency and interpretability. Third, with a built-in cross-validation procedure, it avoids specification searches and thus is easy to implement. An empirical example of Election Day Registration and voter turnout in the United States is provided.},
	language = {en},
	number = {01},
	urldate = {2018-10-23},
	journal = {Political Analysis},
	author = {Xu, Yiqing},
	month = jan,
	year = {2017},
	pages = {57--76},
	file = {Xu_PA_2017.pdf:/Users/michael/Zotero/storage/B5MEIDNK/Xu_PA_2017.pdf:application/pdf}
}

@article{galiani2017synth_runner,
  title={The synth\_runner package: Utilities to automate synthetic control estimation using synth},
  author={Galiani, Sebastian and Quistorff, Brian},
  journal={The Stata Journal},
  volume={17},
  number={4},
  pages={834--849},
  year={2017},
  publisher={SAGE Publications Sage CA: Los Angeles, CA}
}

@article{ben_michael_augmented_2018,
	title = {The {Augmented} {Synthetic} {Control} {Method}},
	url = {http://arxiv.org/abs/1811.04170},
	abstract = {The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The "synthetic control" is a weighted average of control units that balances the treated unit's pre-treatment outcomes as closely as possible. The curse of dimensionality, however, means that SCM does not generally achieve exact balance, which can bias the SCM estimate. We propose an extension, Augmented SCM, which uses an outcome model to estimate the bias due to covariate imbalance and then de-biases the original SCM estimate, analogous to bias correction for inexact matching. We motivate this approach by showing that SCM is a (regularized) inverse propensity score weighting estimator, with pre-treatment outcomes as covariates and a ridge penalty on the propensity score coefficients. We give theoretical guarantees for specific cases and propose a new inference procedure. We demonstrate gains from Augmented SCM with extensive simulation studies and apply this framework to canonical SCM examples. We implement the proposed method in the new augsynth R package.},
	urldate = {2018-12-03},
	journal = {arXiv:1811.04170 [econ, stat]},
	author = {Ben-Michael, Eli and Feller, Avi and Rothstein, Jesse},
	month = nov,
	year = {2018},
	note = {arXiv: 1811.04170},
	keywords = {Statistics - Methodology, Economics - Econometrics},
	file = {arXiv\:1811.04170 PDF:/Users/michael/Zotero/storage/QU2GRND9/Ben-Michael et al. - 2018 - The Augmented Synthetic Control Method.pdf:application/pdf;arXiv.org Snapshot:/Users/michael/Zotero/storage/LZMUR2PD/1811.html:text/html}
}

@article{abadie_synthetic_2010,
	title = {Synthetic {Control} {Methods} for {Comparative} {Case} {Studies}: {Estimating} the {Effect} of {California}’s {Tobacco} {Control} {Program}},
	volume = {105},
	issn = {0162-1459, 1537-274X},
	shorttitle = {Synthetic {Control} {Methods} for {Comparative} {Case} {Studies}},
	url = {http://www.tandfonline.com/doi/abs/10.1198/jasa.2009.ap08746},
	doi = {10.1198/jasa.2009.ap08746},
	language = {en},
	number = {490},
	urldate = {2019-05-03},
	journal = {Journal of the American Statistical Association},
	author = {Abadie, Alberto and Diamond, Alexis and Hainmueller, Jens},
	month = jun,
	year = {2010},
	pages = {493--505},
	file = {Abadie et al. - 2010 - Synthetic Control Methods for Comparative Case Stu.pdf:/Users/michael/Zotero/storage/KMLGHRMI/Abadie et al. - 2010 - Synthetic Control Methods for Comparative Case Stu.pdf:application/pdf}
}

@article{ferman_cherry_nodate,
	title = {Cherry {Picking} with {Synthetic} {Controls}},
	abstract = {We evaluate whether a lack of guidance on how to choose the matching variables used in the Synthetic Control (SC) estimator creates speciﬁcation-searching opportunities. We ﬁrst provide theoretical results showing that speciﬁcation-searching opportunities would be asymptotically irrelevant when the number of pre-treatment periods goes to inﬁnity when we restrict to a subset of SC speciﬁcations. However, based on Monte Carlo simulations and simulations with real datasets, we show signiﬁcant room for speciﬁcation searching when the number of pre-treatment periods is ﬁnite and when alternative speciﬁcations commonly used in SC applications are also considered. This undermines one of the potential advantages of the method, which is providing a transparent way of choosing comparison units and, therefore, being less susceptible to speciﬁcation searching than alternative methods. To address this problem, we provide recommendations to limit the possibilities for speciﬁcation searching in the SC method. Finally, we analyze the possibilities for speciﬁcation searching and our recommendations in two empirical applications.},
	language = {en},
	author = {Ferman, Bruno and Pinto, Cristine and Possebom, Vitor},
	pages = {58},
	file = {Ferman et al. - Cherry Picking with Synthetic Controls.pdf:/Users/michael/Zotero/storage/2HXI3SAP/Ferman et al. - Cherry Picking with Synthetic Controls.pdf:application/pdf}
}

@article{klosner_comparative_2018,
	title = {Comparative politics and the synthetic control method revisited: a note on {Abadie} et al. (2015)},
	volume = {154},
	issn = {2235-6282},
	shorttitle = {Comparative politics and the synthetic control method revisited},
	url = {https://sjes.springeropen.com/articles/10.1186/s41937-017-0004-9},
	doi = {10.1186/s41937-017-0004-9},
	abstract = {Recently, Abadie et al. (Am J Polit Sci 59:495–510, 2015) have expanded synthetic control methods by the so-called cross-validation technique. We find that their results are not being reproduced when alternative software packages are used or when the variables’ ordering within the dataset is changed. We show that this failure stems from the cross-validation technique relying on non-uniquely defined predictor weights. While the amount of the resulting ambiguity is negligible for the main application of Abadie et al. (Am J Polit Sci 59:495–510, 2015), we find it to be substantial for several of their robustness analyses. Applying well-defined, standard synthetic control methods reveals that the authors’ results are particularly driven by a specific control country, the USA.},
	language = {en},
	number = {1},
	urldate = {2019-07-08},
	journal = {Swiss Journal of Economics and Statistics},
	author = {Klößner, Stefan and Kaul, Ashok and Pfeifer, Gregor and Schieler, Manuel},
	month = dec,
	year = {2018},
	pages = {11},
	file = {Klößner et al. - 2018 - Comparative politics and the synthetic control met.pdf:/Users/michael/Zotero/storage/L9HJVXM4/Klößner et al. - 2018 - Comparative politics and the synthetic control met.pdf:application/pdf}
}

@article{kinn_synthetic_2018,
	title = {Synthetic {Control} {Methods} and {Big} {Data}},
	url = {http://arxiv.org/abs/1803.00096},
	abstract = {Many macroeconomic policy questions may be assessed in a case study framework, where the time series of a treated unit is compared to a counterfactual constructed from a large pool of control units. I provide a general framework for this setting, tailored to predict the counterfactual by minimizing a tradeoﬀ between underﬁtting (bias) and overﬁtting (variance). The framework nests recently proposed structural and reduced form machine learning approaches as special cases. Furthermore, diﬀerence-in-diﬀerences with matching and the original synthetic control are restrictive cases of the framework, in general not minimizing the bias-variance objective. Using simulation studies I ﬁnd that machine learning methods outperform traditional methods when the number of potential controls is large or the treated unit is substantially diﬀerent from the controls. Equipped with a toolbox of approaches, I revisit a study on the eﬀect of economic liberalisation on economic growth. I ﬁnd eﬀects for several countries where no eﬀect was found in the original study. Furthermore, I inspect how a systematically important bank respond to increasing capital requirements by using a large pool of banks to estimate the counterfactual. Finally, I assess the eﬀect of a changing product price on product sales using a novel scanner dataset.},
	language = {en},
	urldate = {2019-07-31},
	journal = {arXiv:1803.00096 [econ]},
	author = {Kinn, Daniel},
	month = feb,
	year = {2018},
	note = {arXiv: 1803.00096},
	keywords = {Economics - Econometrics},
	file = {Kinn - 2018 - Synthetic Control Methods and Big Data.pdf:/Users/michael/Zotero/storage/KLTWL9XX/Kinn - 2018 - Synthetic Control Methods and Big Data.pdf:application/pdf}
}

@article{abadie_economic_2003,
	title = {The {Economic} {Costs} of {Conflict}: {A} {Case} {Study} of the {Basque} {Country}},
	volume = {93},
	issn = {0002-8282},
	shorttitle = {The {Economic} {Costs} of {Conflict}},
	url = {http://pubs.aeaweb.org/doi/10.1257/000282803321455188},
	doi = {10.1257/000282803321455188},
	language = {en},
	number = {1},
	urldate = {2019-10-03},
	journal = {American Economic Review},
	author = {Abadie, Alberto and Gardeazabal, Javier},
	month = feb,
	year = {2003},
	pages = {113--132},
	file = {Abadie and Gardeazabal - 2003 - The Economic Costs of Conflict A Case Study of th.pdf:/Users/michael/Zotero/storage/I5YFF5PX/Abadie and Gardeazabal - 2003 - The Economic Costs of Conflict A Case Study of th.pdf:application/pdf}
}

 @article{Synth,
    title = {{Synth}: An {R} Package for Synthetic Control Methods in Comparative Case Studies},
    author = {Alberto Abadie and Alexis Diamond and Jens Hainmueller},
    journal = {Journal of Statistical Software},
    year = {2011},
    volume = {42},
    number = {13},
    doi = {10.18637/jss.v042.i13}
    pages = {1--17},
    url = {http://www.jstatsoft.org/v42/i13/}
    }
    
@article{Kreif2015,
	abstract = {Despite its centrality for the provision of health care, physician compensation remains understudied, and existing studies either fail to control for time trends, cover small samples from highly particular settings, or examine empirically negligible changes in reward levels. Using a four-year sample of 59 physicians and 1.1 million encounters, we study how physicians at a network of primary care clinics responded when their salaried compensation plan was replaced with a lower salary plus substantial piece rates for encounters and select procedures. Although patient characteristics remained unchanged, physicians increased encounters by 11 to 61{\%}, both by increasing encounters per day and days worked at the network, and increased procedures to the maximum reimbursable level.},
	author = {Kreif, No{\'{e}}mi and Grieve, Richard and Hangartner, Dominik and Turner, Alex James and Nikolova, Silviya and Sutton, Matt},
	doi = {10.1002/hec.3258},
	journal = {Health economics},
	pages = {1--16},
	title = {{How sensitive is physician performance to alternative compensation schedules? Evidence from a large network of primary care clinics.}},
	year = {2015}
}

@article{Cavallo2013,
	abstract = {We examine the average causal impact of catastrophic natural disasters on economic growth by combining information from comparative case studies. For each country affected by a large disaster, we compute the counterfactual by constructing synthetic controls. We find that only extremely large disasters have a negative effect on output in both the short and the long runs. However, we also show that this results from two events where radical political revolutions followed the disasters. Once we control for these political changes, even extremely large disasters do not display any significant effect on economic growth.},
	author = {Cavallo, Eduardo and Galiani, Sebastian and Noy, Ilan and Pantano, Juan},
	journal = {Review of Economics and Statistics},
	keywords = {costs,geography,impact,income,rare disasters},
	number = {5},
	pages = {1549--1561},
	title = {{Catastrophic Natural Disasters and Economic Growth}},
	volume = {95},
	year = {2013}
}

@article{abadie_comparative_2015,
	title = {Comparative {Politics} and the {Synthetic} {Control} {Method}},
	volume = {59},
	copyright = {©2014, Midwest Political Science Association},
	issn = {1540-5907},
	url = {http://onlinelibrary.wiley.com/doi/abs/10.1111/ajps.12116},
	doi = {10.1111/ajps.12116},
	abstract = {In recent years, a widespread consensus has emerged about the necessity of establishing bridges between quantitative and qualitative approaches to empirical research in political science. In this article, we discuss the use of the synthetic control method as a way to bridge the quantitative/qualitative divide in comparative politics. The synthetic control method provides a systematic way to choose comparison units in comparative case studies. This systematization opens the door to precise quantitative inference in small-sample comparative studies, without precluding the application of qualitative approaches. Borrowing the expression from Sidney Tarrow, the synthetic control method allows researchers to put “qualitative flesh on quantitative bones.” We illustrate the main ideas behind the synthetic control method by estimating the economic impact of the 1990 German reunification on West Germany.},
	language = {en},
	number = {2},
	urldate = {2019-10-03},
	journal = {American Journal of Political Science},
	author = {Abadie, Alberto and Diamond, Alexis and Hainmueller, Jens},
	year = {2015},
	keywords = {comparative case studies, difference-in-differences, German reunification, matching, synthetic control method},
	pages = {495--510},
	file = {Snapshot:/Users/michael/Zotero/storage/JXAJPGJI/ajps.html:text/html}
}

@Manual{furr_package,
    title = {furrr: Apply Mapping Functions in Parallel using Futures},
    author = {Davis Vaughan and Matt Dancho},
    year = {2018},
    note = {R package version 0.1.0},
    url = {https://CRAN.R-project.org/package=furrr},
  }
  
  @Manual{future_package,
    title = {future: Unified Parallel and Distributed Processing in R for Everyone},
    author = {Henrik Bengtsson},
    year = {2019},
    note = {R package version 1.14.0},
    url = {https://CRAN.R-project.org/package=future},
  }