Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models

doi:10.1017/pan.2016.2

journal article Jan 01, 2017

Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models

Political Analysis Vol. 25 No. 1 pp. 57-76 · Cambridge University Press (CUP)

View at Publisher Save 10.1017/pan.2016.2

Abstract

Difference-in-differences (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 often-violated assumption, but also unifies the synthetic control method (Abadie, Diamond, and Hainmueller 2010) with linear fixed effects 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 effects model that incorporates unit-specific intercepts interacted with time-varying coefficients. 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 efficiency 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.

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References

54

[1]

10.1023/a:1006638400784

[2]

10.1093/pan/mpr021

[3]

10.1177/1532673x11432470

[4]

10.1016/j.jfineco.2015.10.001

[5]

10.3386/w19355

[6]

10.1093/pan/mps041

[7]

10.2307/2585934

[8]

10.1037/h0037350

[9]

Leighley (2013) 10.23943/princeton/9780691159348.001.0001

[10]

10.7208/chicago/9780226114354.001.0001

[11]

Dube, Arindrajit , and Ben Zipperer . 2015. Pooling multiple case studies using synthetic controls: An application to minimum wage policies. IZA Discussion Paper No. 8944. 10.2139/ssrn.2589786

[12]

Campbell (1997) 10.1515/9781400830213

[13]

10.1177/1532673x9402200105

[14]

10.1111/j.1540-5907.2012.00590.x

[15]

Wolfinger (1980)

[16]

Cain (2011)

[17]

10.1080/01621459.1986.10478354

[18]

10.1093/pan/mpq019

[19]

Alvarez, R. Michael , Stephen Ansolabehere , and Catherine H. Wilson . 2002. Election day voter registration in the United States: How one-step voting can change the composition of the American electorate. Working Paper, Caltech/MIT Voting Technology Project.

[20]

10.1093/pan/mpt029

[21]

Knack "Election-day registration The second wave" American Politics Research (2001)

[22]

10.1111/ajps.12116

[23]

10.1111/0034-6527.00321

[24]

10.1017/s0266466615000328

[25]

Burnham (1980)

[26]

Bai "Panel data models with interactive fixed effects" Econometrica (2009) 10.3982/ecta6135

[27]

What To Do (and Not to Do) with Time-Series Cross-Section Data

Nathaniel Beck, Jonathan N. Katz

American Political Science Review 10.2307/2082979

[28]

Stewart, Brandon . 2014. Latent factor regressions for the social sciences. Princeton University. Mimeo.

[29]

10.1198/jasa.2009.ap08746

[30]

10.1002/jae.1230

[31]

10.1162/rest_a_00537

[32]

10.1017/cbo9780511605338

[33]

10.1017/s0022381600053585

[34]

10.1111/0038-4941.00015

[35]

10.7910/dvn/8akacj

[36]

Imai, Kosuke , and In Song Kim . 2016. When should we use linear fixed effects regression models for causal inference with panel data. Princeton University. Mimeo.

[37]

Burden, Barry C. , David T. Canon , Kenneth R. Mayer , and Donald P. Moynihan . 2009. The effects and costs of early voting, election day registration, and same day registration in the 2008 elections. University of Wisconsin-Madison. Mimeo.

[38]

Mora, Ricardo , and Ilina Reggio . 2012. Treatment effect identification using alternative parallel assumptions. Universidad Carlos III de Madrid. Mimeo.

[39]

Teixeira (2011)

[40]

10.1007/bf01498813

[41]

10.1111/j.1541-1338.1995.tb00711.x

[42]

10.1093/pan/10.2.154

[43]

Blackwell, Matthew , and Adam Glynn . 2015. How to make causal inferences with time-series cross-sectional data. Harvard University. Mimeo.

[44]

10.1214/ss/1177012031

[45]

10.1111/1468-0262.00392

[46]

10.1093/pan/mpu008

[47]

10.1111/j.1540-5907.2010.00459.x

[48]

10.1017/s1537592704040307

[49]

10.1007/bf01498789

[50]

10.1198/016214504000000692

Showing 50 of 54 references

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Details

Published: Jan 01, 2017
Vol/Issue: 25(1)
Pages: 57-76
License: View

Cite This Article

(2017). Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models. Political Analysis, 25(1), 57-76. https://doi.org/10.1017/pan.2016.2

Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models

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