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Estimating Treatment Effects With Artificial Neural Nets – A Comparison to Synthetic Control Method

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  • Arne Steinkraus

    (Institute of Economics Braunschweig)

Abstract

With the advent of big data in economics machine learning algorithms become more and more appealing to economists. Despite some attempts of establishing artificial neural networks in in the early 1990s, only little is known about their ability of estimating causal effects. We employ a simple forecasting neural network to analyze the effect of the construction of the Oresund bridge on the local economy. The outcome is compared to the causal effect estimated by the proven Synthetic Control Method. Our results suggest that neural nets may outperform traditional approaches.

Suggested Citation

  • Arne Steinkraus, 2019. "Estimating Treatment Effects With Artificial Neural Nets – A Comparison to Synthetic Control Method," Economics Bulletin, AccessEcon, vol. 39(4), pages 2778-2791.
    • Handle: RePEc:ebl:ecbull:eb-19-00684
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    References listed on IDEAS

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    More about this item

    Keywords

    Artificial Neural Nets; Machine Learning; Synthetic Control Method; Policy Evaluation;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development

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