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Comparative Effectiveness of Machine Learning Methods for Causal Inference in Agricultural Economics

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  • Badruddoza, Syed
  • Fuad, Syed
  • Amin, Modhurima D.

Abstract

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Suggested Citation

  • Badruddoza, Syed & Fuad, Syed & Amin, Modhurima D., 2023. "Comparative Effectiveness of Machine Learning Methods for Causal Inference in Agricultural Economics," 2023 Annual Meeting, July 23-25, Washington D.C. 335782, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea22:335782
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    File URL: https://ageconsearch.umn.edu/record/335782/files/26952.pdf
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    References listed on IDEAS

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    1. Gharad Bryan & Shyamal Chowdhury & Ahmed Mushfiq Mobarak, 2014. "Underinvestment in a Profitable Technology: The Case of Seasonal Migration in Bangladesh," Econometrica, Econometric Society, vol. 82(5), pages 1671-1748, September.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    3. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
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    More about this item

    Keywords

    Research Methods/Statistical Methods; Food Consumption/Nutrition/Food Safety; Agricultural and Food Policy;
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