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

Yusuke Narita (), Shunsuke Aihara, Yuta Saito, Megumi Matsutani and Kohei Yata

Discussion Papers (Japanese) from Research Institute of Economy, Trade and Industry (RIETI)

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.

Pages: 24 pages
Date: 2020-12
New Economics Papers: this item is included in nep-big and nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:eti:rdpsjp:20045

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