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Ensemble Methods for Causal Effects in Panel Data Settings

Author

Listed:
  • Susan Athey
  • Mohsen Bayati
  • Guido Imbens
  • Zhaonan Qu

Abstract

This paper studies a panel data setting where the goal is to estimate causal effects of an intervention by predicting the counterfactual values of outcomes for treated units, had they not received the treatment. Several approaches have been proposed for this problem, including regression methods, synthetic control methods and matrix completion methods. This paper considers an ensemble approach, and shows that it performs better than any of the individual methods in several economic datasets. Matrix completion methods are often given the most weight by the ensemble, but this clearly depends on the setting. We argue that ensemble methods present a fruitful direction for further research in the causal panel data setting.

Suggested Citation

  • Susan Athey & Mohsen Bayati & Guido Imbens & Zhaonan Qu, 2019. "Ensemble Methods for Causal Effects in Panel Data Settings," Papers 1903.10079, arXiv.org.
    • Handle: RePEc:arx:papers:1903.10079
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    References listed on IDEAS

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    1. Susan Athey & Mohsen Bayati & Nikolay Doudchenko & Guido Imbens & Khashayar Khosravi, 2021. "Matrix Completion Methods for Causal Panel Data Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1716-1730, October.
    2. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    3. Nikolay Doudchenko & Guido W. Imbens, 2016. "Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis," NBER Working Papers 22791, National Bureau of Economic Research, Inc.
    4. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    5. repec:cup:cbooks:9780521885881 is not listed on IDEAS
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    Cited by:

    1. Fetzer, Thiemo & Wang, Shizhuo, 2020. "Measuring the Regional Economic Cost of Brexit: Evidence up to 2019," CEPR Discussion Papers 15051, C.E.P.R. Discussion Papers.
    2. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021. "Economic Predictions With Big Data: The Illusion of Sparsity," Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
    3. Gert Bijnens & Shyngys Karimov & Jozef Konings, 2023. "Does Automatic Wage Indexation Destroy Jobs? A Machine Learning Approach," De Economist, Springer, vol. 171(1), pages 85-117, March.
    4. Newell, Richard G. & Prest, Brian C. & Sexton, Steven E., 2021. "The GDP-Temperature relationship: Implications for climate change damages," Journal of Environmental Economics and Management, Elsevier, vol. 108(C).
    5. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," Working Paper Series 2830, European Central Bank.
    6. Rong J. B. Zhu, 2023. "Synthetic Regressing Control Method," Papers 2306.02584, arXiv.org, revised Oct 2023.
    7. Jesús Fernández-Villaverde, 2021. "Has machine learning rendered simple rules obsolete?," European Journal of Law and Economics, Springer, vol. 52(2), pages 251-265, December.
    8. Daníelsson, Jón & Macrae, Robert & Uthemann, Andreas, 2022. "Artificial intelligence and systemic risk," Journal of Banking & Finance, Elsevier, vol. 140(C).
    9. Shafiullah Qureshi & Ba Chu & Fanny S. Demers, 2021. "Forecasting Canadian GDP Growth with Machine Learning," Carleton Economic Papers 21-05, Carleton University, Department of Economics.
    10. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    11. Povilas Lastauskas & Julius Stakenas, 2019. "Does It Matter When Labor Market Reforms Are Implemented? The Role of the Monetary Policy Environment," Bank of Lithuania Working Paper Series 66, Bank of Lithuania.
    12. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    13. Cerqua, Augusto & Letta, Marco, 2022. "Local inequalities of the COVID-19 crisis," Regional Science and Urban Economics, Elsevier, vol. 92(C).
    14. Duarte, Victor & Duarte, Diogo & Fonseca, Julia & Montecinos, Alexis, 2020. "Benchmarking machine-learning software and hardware for quantitative economics," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    15. Surender Kumar & Madhu Khanna, 2019. "Temperature and production efficiency growth: empirical evidence," Climatic Change, Springer, vol. 156(1), pages 209-229, September.
    16. Jesus Fernandez-Villaverde, 2020. "Simple Rules for a Complex World with Arti?cial Intelligence," PIER Working Paper Archive 20-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    17. Cerqua, Augusto & Letta, Marco, 2020. "Local economies amidst the COVID-19 crisis in Italy: a tale of diverging trajectories," MPRA Paper 104404, University Library of Munich, Germany.
    18. Mika Ylinen & Mikko Ranta, 2024. "Employer ratings in social media and firm performance: Evidence from an explainable machine learning approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 247-276, March.
    19. Hollingsworth, Alex & Wing, Coady, 2020. "Tactics for design and inference in synthetic control studies: An applied example using high-dimensional data," SocArXiv fc9xt, Center for Open Science.
    20. Viviano, Davide & Bradic, Jelena, 2023. "Synthetic Learner: Model-free inference on treatments over time," Journal of Econometrics, Elsevier, vol. 234(2), pages 691-713.
    21. Gabriel Loumeau & Christian Stettler, 2021. "Fiscal Autonomy and Self-Determination," CESifo Working Paper Series 9445, CESifo.

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    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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