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Ensemble Methods for Causal Effects in Panel Data Settings

Author

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  • Susan Athey
  • Mohsen Bayati
  • Guido Imbens
  • Zhaonan Qu
Abstract
In many prediction problems researchers have found that combinations of prediction methods (“ensembles”) perform better than individual methods. A simple example is random forests, which combines predictions from many regression trees. A striking, and substantially more complex, example is the Netflix Prize competition where the winning entry combined predictions using a wide variety of conceptually very different models. In macro-economic forecasting researchers have often found that averaging predictions from different models leads to more accurate forecasts. In this paper we apply these ideas to synthetic control type problems in panel data setting. In this setting a number of conceptually quite different methods have been developed, with some assuming correlations between units that are stable over time, others assuming stable time series patterns common to all units, and others using factor models. With data on state level GDP for 270 quarters, we focus on three basic approaches to predicting missing values, one from each of these strands of the literature. Rather than try to test the different models against each other and find a true model, we focus on combining predictions based on each of the separate models using ensemble methods. For the ensemble predictor we focus on a weighted average of the three individual methods, with non-negative weights determined through out-of-sample cross-validation.

Suggested Citation

  • Susan Athey & Mohsen Bayati & Guido Imbens & Zhaonan Qu, 2019. "Ensemble Methods for Causal Effects in Panel Data Settings," NBER Working Papers 25675, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:25675
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    5. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, September.
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    Cited by:

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    2. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    3. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," CEPR Discussion Papers 18298, C.E.P.R. Discussion Papers.
    4. 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.
    5. 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.
    6. Daníelsson, Jón & Macrae, Robert & Uthemann, Andreas, 2022. "Artificial intelligence and systemic risk," Journal of Banking & Finance, Elsevier, vol. 140(C).
    7. 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.
    8. 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.
    9. Surender Kumar & Madhu Khanna, 2019. "Temperature and production efficiency growth: empirical evidence," Climatic Change, Springer, vol. 156(1), pages 209-229, September.
    10. Gabriel Loumeau & Christian Stettler, 2021. "Fiscal Autonomy and Self-Determination," CESifo Working Paper Series 9445, CESifo.
    11. 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.
    12. Cerqua, Augusto & Letta, Marco, 2022. "Local inequalities of the COVID-19 crisis," Regional Science and Urban Economics, Elsevier, vol. 92(C).
    13. 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.
    14. 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.
    15. 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).
    16. Viviano, Davide & Bradic, Jelena, 2023. "Synthetic Learner: Model-free inference on treatments over time," Journal of Econometrics, Elsevier, vol. 234(2), pages 691-713.
    17. Rong J. B. Zhu, 2023. "Synthetic Regressing Control Method," Papers 2306.02584, arXiv.org, revised Oct 2023.
    18. 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.
    19. 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).
    20. 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.
    21. 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.

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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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