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Implementing machine learning methods in Stata

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  • Austin Nichols

    (Abt Associates)

Abstract

This presentation will discuss some popular supervised and unsupervised machine learning algorithms, and their recommended use, and then present implementations in Stata. The emphasis is on prediction and causal inference, and how to tailor a method to a specific application.

Suggested Citation

  • Austin Nichols, 2018. "Implementing machine learning methods in Stata," London Stata Conference 2018 08, Stata Users Group.
    • Handle: RePEc:boc:usug18:08
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    File URL: http://repec.org/usug2018/Nichols18.pdf
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    References listed on IDEAS

    as
    1. Austin Nichols & Linden McBride, 2017. "Propensity Scores and Causal Inference Using Machine Learning Methods," 2017 Stata Conference 13, Stata Users Group.
    2. Linden McBride & Austin Nichols, 2018. "Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning," The World Bank Economic Review, World Bank, vol. 32(3), pages 531-550.
    3. Hiren Nisar, 2017. "Analyzing satellite data in Stata," 2017 Stata Conference 22, Stata Users Group.
    Full references (including those not matched with items on IDEAS)

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