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Random forests and selected samples

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  • Jonathan A. Cook
  • Saad Siddiqui

Abstract

This paper presents a procedure for recovering causal coefficients from selected samples that uses random forests, a popular machine‐learning algorithm. This proposed method makes few assumptions regarding the selection equation and the distribution of the error terms. Our Monte Carlo results indicate that our method performs well, even when the selection and outcome equations contain the same variables, as long as the selection equation is nonlinear. The method can also be used when there are many variables in the selection equation. We also compare the results of our procedure with other parametric and semiparametric methods using real data.

Suggested Citation

  • Jonathan A. Cook & Saad Siddiqui, 2020. "Random forests and selected samples," Bulletin of Economic Research, Wiley Blackwell, vol. 72(3), pages 272-287, July.
    • Handle: RePEc:bla:buecrs:v:72:y:2020:i:3:p:272-287
    DOI: 10.1111/boer.12222
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    References listed on IDEAS

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    1. Wayne Taylor & Brett Hollenbeck, 2021. "Leveraging loyalty programs using competitor based targeting," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 417-455, December.

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