Fetching the paper…
Reading the bibliography…
The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings.
Minimax linear estimation of the retargeted mean
Hirshberg, D. A., A. Maleki, and J. Zubizarreta (2019) · 1901
Earlier work this paper cites.
Minimax linear estimation of the retargeted mean
Hirshberg, D. A., A. Maleki, and J. Zubizarreta (2019) · 1901
Earlier work this paper cites.
Predictive inference with the jackknife+
Barber, R. F., E. J. Candes, A. Ramdas, and R. J. Tibshirani (2019) · 1905
Earlier work this paper cites.
Predictive inference with the jackknife+
Barber, R. F., E. J. Candes, A. Ramdas, and R. J. Tibshirani (2019) · 1905
Earlier work this paper cites.
Synthetic controls and weighted event studies with staggered adoption
Ben-Michael, E., A. Feller, and J. Rothstein (2019) · 1912
Earlier work this paper cites.
Prediction intervals for synthetic control methods
Cattaneo, M. D., Y. Feng, and R. Titiunik (2019) · 1912
Earlier work this paper cites.
Synthetic controls and weighted event studies with staggered adoption
Ben-Michael, E., A. Feller, and J. Rothstein (2019) · 1912
Earlier work this paper cites.
Prediction intervals for synthetic control methods
Cattaneo, M. D., Y. Feng, and R. Titiunik (2019) · 1912
Earlier work this paper cites.
On the application of probability theory to agricultural experiments. essay on principles. section 9
Neyman, J. (1990 [1923]) · 1923
Earlier work this paper cites.
On the application of probability theory to agricultural experiments. essay on principles. section 9
Neyman, J. (1990 [1923]) · 1923
Earlier work this paper cites.
The use of matched sampling and regression adjustment to remove bias in observational studies
Rubin, D. B. (1973) · 1973
Earlier work this paper cites.
The use of matched sampling and regression adjustment to remove bias in observational studies
Rubin, D. B. (1973) · 1973
Earlier work this paper cites.
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · 1974
Earlier work this paper cites.
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · 1974
Earlier work this paper cites.
Some results on generalized difference estimation and generalized regression estimation for finite populations
Cassel, C. M., C.-E. Sarndal, and J. H. Wretman (1976) · 1976
Earlier work this paper cites.
Some results on generalized difference estimation and generalized regression estimation for finite populations
Cassel, C. M., C.-E. Sarndal, and J. H. Wretman (1976) · 1976
Earlier work this paper cites.
Comment on “randomization analysis of experimental data: The fisher randomization test”
Rubin, D. B. (1980) · 1980
Earlier work this paper cites.
Comment on “randomization analysis of experimental data: The fisher randomization test”
Rubin, D. B. (1980) · 1980
Earlier work this paper cites.
Estimation of regression coefficients when some regressors are not always observed
Robins, J. M., A. Rotnitzky, and L. P. Zhao (1994) · 1994
Earlier work this paper cites.
Estimation of regression coefficients when some regressors are not always observed
Robins, J. M., A. Rotnitzky, and L. P. Zhao (1994) · 1994
Earlier work this paper cites.
The Economic Costs of Conflict: A Case Study of the Basque Country
Abadie, A. and J. Gardeazabal (2003) · 2003
Earlier work this paper cites.
The Economic Costs of Conflict: A Case Study of the Basque Country
Abadie, A. and J. Gardeazabal (2003) · 2003
Earlier work this paper cites.
Algorithmic learning in a random world
Vovk, V., A. Gammerman, and G. Shafer (2005) · 2005
Earlier work this paper cites.
Algorithmic learning in a random world
Vovk, V., A. Gammerman, and G. Shafer (2005) · 2005
Earlier work this paper cites.
The dangers of extreme counterfactuals
King, G. and L. Zeng (2006) · 2006
Earlier work this paper cites.
The dangers of extreme counterfactuals
King, G. and L. Zeng (2006) · 2006
Earlier work this paper cites.
Panel data models with interactive fixed effects
Bai, J. (2009) · 2009
Earlier work this paper cites.
The elements of statistical learning
Hastie, T., J. Friedman, and R. Tibshirani (2009) · 2009
Earlier work this paper cites.
Panel data models with interactive fixed effects
Bai, J. (2009) · 2009
Earlier work this paper cites.
The elements of statistical learning
Hastie, T., J. Friedman, and R. Tibshirani (2009) · 2009
Earlier work this paper cites.
Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program
Abadie, A., A. Diamond, and J. Hainmueller (2010) · 2010
Earlier work this paper cites.
Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program
Abadie, A., A. Diamond, and J. Hainmueller (2010) · 2010
Earlier work this paper cites.
Bias-corrected matching estimators for average treatment effects
Abadie, A. and G. W. Imbens (2011) · 2011
Earlier work this paper cites.
Oaxaca-Blinder as a reweighting estimator
Kline, P. (2011) · 2011
Earlier work this paper cites.
Bias-corrected matching estimators for average treatment effects
Abadie, A. and G. W. Imbens (2011) · 2011
Earlier work this paper cites.
Oaxaca-Blinder as a reweighting estimator
Kline, P. (2011) · 2011
Earlier work this paper cites.
Comparative Politics and the Synthetic Control Method
Abadie, A., A. Diamond, and J. Hainmueller (2015) · 2015
Earlier work this paper cites.
Inferring Causal Impact using Bayesian Structural Time-Series Models
Brodersen, K. H., F. Gallusser, J. Koehler, N. Remy, and S. L. Scott (2015) · 2015
Earlier work this paper cites.
Pooling multiple case studies using synthetic controls: An application to minimum wage policies
Dube, A. and B. Zipperer (2015) · 2015
Earlier work this paper cites.
Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data
Zubizarreta, J. R. (2015) · 2015
Earlier work this paper cites.
Comparative Politics and the Synthetic Control Method
Abadie, A., A. Diamond, and J. Hainmueller (2015) · 2015
Cited alongside, same era.
Inferring Causal Impact using Bayesian Structural Time-Series Models
Brodersen, K. H., F. Gallusser, J. Koehler, N. Remy, and S. L. Scott (2015) · 2015
Cited alongside, same era.
Pooling multiple case studies using synthetic controls: An application to minimum wage policies
Dube, A. and B. Zipperer (2015) · 2015
Cited alongside, same era.
Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data
Zubizarreta, J. R. (2015) · 2015
Cited alongside, same era.
Regional policy evaluation: Interactive fixed effects and synthetic controls
Gobillon, L. and T. Magnac (2016) · 2016
Cited alongside, same era.
Examination of the synthetic control method for evaluating health policies with multiple treated units
Randomization tests in observational studies with time-varying adoption of treatment
Toulis, P. and A. Shaikh (2018) · 2018
Closest in time.
High dimensional statistics: a non-asymptomatic viewpoint
Wainwright, M. (2018) · 2018
Closest in time.
Minimal Approximately Balancing Weights: Asymptotic Properties and Practical Considerations
Wang, Y. and J. R. Zubizarreta (2018) · 2018
Closest in time.
Covariate Balancing Propensity Score by Tailored Loss Functions
Zhao, Q. (2018) · 2018
Closest in time.
A penalized synthetic control estimator for disaggregated data
Abadie, A. and J. L’Hour (2018) · 2018
Closest in time.
Robust synthetic control
Amjad, M., D. Shah, and D. Shen (2018) · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kreif, N., R. Grieve, D. Hangartner, A. J. Turner, S. Nikolova, and M. Sutton (2016) · 2016
Cited alongside, same era.
Regional policy evaluation: Interactive fixed effects and synthetic controls
Gobillon, L. and T. Magnac (2016) · 2016
Cited alongside, same era.
Examination of the synthetic control method for evaluating health policies with multiple treated units
Kreif, N., R. Grieve, D. Hangartner, A. J. Turner, S. Nikolova, and M. Sutton (2016) · 2016
Cited alongside, same era.
Matrix Completion Methods for Causal Panel Data Models
Athey, S., M. Bayati, N. Doudchenko, G. Imbens, and K. Khosravi (2017) · 2017
Cited alongside, same era.
The state of applied econometrics: Causality and policy evaluation
Athey, S. and G. W. Imbens (2017) · 2017
Cited alongside, same era.
Model-Assisted Survey Estimation with Modern Prediction Techniques
Breidt, F. J. and J. D. Opsomer (2017) · 2017
Cited alongside, same era.
Right-to-carry laws and violent crime: A comprehensive assessment using panel data and a state-level synthetic control analysis
Donohue, J. J., A. Aneja, and K. D. Weber (2017) · 2017
Cited alongside, same era.
Approximate residual balancing: debiased inference of average treatment effects in high dimensions
Athey, S., G. W. Imbens, and S. Wager (2018) · 2018
Closest in time.
Inference on average treatment effects in aggregate panel data settings
Chernozhukov, V., K. Wuthrich, and Y. Zhu (2018) · 2018
Closest in time.
Synthetic controls with imperfect pre-treatment fit
Ferman, B. and C. Pinto (2018) · 2018
Closest in time.
Trajectory balancing: A general reweighting approach to causal inference with time-series cross-sectional data
Hazlett, C. and Y. Xu (2018) · 2018
Closest in time.
Augmented Minimax Linear Estimation
Hirshberg, D. A. and S. Wager (2018) · 2018
Closest in time.
Panel parametric, semi-parametric and nonparametric construction of counterfactuals-california tobacco control revisited
Hsiao, C., Q. Zhou, et al. (2018) · 2018
Closest in time.
Dispersion-weighted synthetic controls
Minard, S. and G. R. Waddell (2018) · 2018
Closest in time.
Imperfect synthetic controls: Did the massachusetts health care reform save lives?
Powell, D. (2018) · 2018
Closest in time.
Two tales of two us states: Regional fiscal austerity and economic performance
Rickman, D. S. and H. Wang (2018) · 2018
Closest in time.
Randomization tests in observational studies with time-varying adoption of treatment
Toulis, P. and A. Shaikh (2018) · 2018
Closest in time.
High dimensional statistics: a non-asymptomatic viewpoint
Wainwright, M. (2018) · 2018
Closest in time.
Minimal Approximately Balancing Weights: Asymptotic Properties and Practical Considerations
Wang, Y. and J. R. Zubizarreta (2018) · 2018
Closest in time.
Covariate Balancing Propensity Score by Tailored Loss Functions
Zhao, Q. (2018) · 2018
Closest in time.
Using synthetic controls: Feasibility, data requirements, and methodological aspects
Abadie, A. (2019) · 2019
Closest in time.
Synthetic difference in differences
Arkhangelsky, D., S. Athey, D. A. Hirshberg, G. W. Imbens, and S. Wager (2019) · 2019
Closest in time.
On the role of covariates in the synthetic control method
Botosaru, I. and B. Ferman (2019) · 2019
Closest in time.
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls
Chernozhukov, V., K. Wüthrich, and Y. Zhu (2019) · 2019
Closest in time.
On the Properties of the Synthetic Control Estimator with Many Periods and Many Controls
Ferman, B. (2019) · 2019
Closest in time.
Assessing the causal effect of binary interventions from observational panel data with few treated units
Samartsidis, P., S. R. Seaman, A. M. Presanis, M. Hickman, D. De Angelis, et al. (2019) · 2019
Closest in time.
Using synthetic controls: Feasibility, data requirements, and methodological aspects
Abadie, A. (2019) · 2019
Closest in time.
Synthetic difference in differences
Arkhangelsky, D., S. Athey, D. A. Hirshberg, G. W. Imbens, and S. Wager (2019) · 2019
Closest in time.
On the role of covariates in the synthetic control method
Botosaru, I. and B. Ferman (2019) · 2019
Closest in time.
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls
Chernozhukov, V., K. Wüthrich, and Y. Zhu (2019) · 2019
Closest in time.
On the Properties of the Synthetic Control Estimator with Many Periods and Many Controls
Ferman, B. (2019) · 2019
Closest in time.
Assessing the causal effect of binary interventions from observational panel data with few treated units
Samartsidis, P., S. R. Seaman, A. M. Presanis, M. Hickman, D. De Angelis, et al. (2019) · 2019
Closest in time.
Goldilocks and the pre-intervention time series
Bilinski, A. and L. Hatfield (2020) · 2020
Closest in time.
Balancing Versus Modeling Approaches to Weighting in Practice
Chattopadhyay, A., Christopher H. Hase, and J. R. Zubizarreta (2020) · 2020
Closest in time.
Combining matching and synthetic controls to trade off biases from extrapolation and interpolation
Kellogg, M., M. Mogstad, G. Pouliot, and A. Torgovitsky (2020) · 2020
Closest in time.
Sensitivity analysis for balancing weights
Soriano, D., E. Ben-Michael, P. Bickel, A. Feller, and S. Pimentel (2020) · 2020
Closest in time.
Goldilocks and the pre-intervention time series
Bilinski, A. and L. Hatfield (2020) · 2020
Closest in time.
Balancing Versus Modeling Approaches to Weighting in Practice
Chattopadhyay, A., Christopher H. Hase, and J. R. Zubizarreta (2020) · 2020
Closest in time.
Combining matching and synthetic controls to trade off biases from extrapolation and interpolation
Kellogg, M., M. Mogstad, G. Pouliot, and A. Torgovitsky (2020) · 2020
Closest in time.
Sensitivity analysis for balancing weights
Soriano, D., E. Ben-Michael, P. Bickel, A. Feller, and S. Pimentel (2020) · 2020
Closest in time.