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Machine Learning as Natural Experiment: Method and Deployment at Japanese Firms (Japanese)

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  • NARITA Yusuke
  • AIHARA Shunsuke
  • SAITO Yuta
  • MATSUTANI Megumi
  • YATA Kohei

Abstract

From public policy to business, machine learning and other algorithms produce a growing portion of treatment decisions and recommendations. Such algorithmic decisions are natural experiments (conditionally quasi-randomly assigned instruments) since the algorithms make decisions based only on observable input variables. We use this observation to characterize the sources of causal-effect identification for a class of stochastic and deterministic algorithms. This identification result translates into consistent estimators of causal effects and the counterfactual performance of new algorithms. We apply our method to improve a large-scale fashion e-commerce platform (ZOZOTOWN). We conclude by providing public policy applications.

Suggested Citation

  • NARITA Yusuke & AIHARA Shunsuke & SAITO Yuta & MATSUTANI Megumi & YATA Kohei, 2020. "Machine Learning as Natural Experiment: Method and Deployment at Japanese Firms (Japanese)," Discussion Papers (Japanese) 20045, Research Institute of Economy, Trade and Industry (RIETI).
    • Handle: RePEc:eti:rdpsjp:20045
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