Estimating density ratio of marginals to joint: applications to causal inference

Yukitoshi Matsushita, Taisuke Otsu and Keisuke Takahata

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: In various fields of data science, researchers often face problems of estimating the ratios of two probability densities. Particularly in the context of causal inference, the product of marginals for a treatment variable and covariates to their joint density ratio typically emerges in the process of constructing causal effect estimators. This article applies the general least square density ratio estimation methodology by Kanamori, Hido and Sugiyama to the product of marginals to joint density ratio, and demonstrates its usefulness particularly for causal inference on continuous treatment effects and dose-response curves. The proposed method is illustrated by a simulation study and an empirical example to investigate the treatment effect of political advertisements in the U.S. presidential campaign data.

Keywords: causal inference; nonparametric methods; smoothing and nonparametric regression (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 15 pages
Date: 2023-04-03
New Economics Papers: this item is included in nep-ore
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Published in Journal of Business and Economic Statistics, 3, April, 2023, 41(2), pp. 467 - 481. ISSN: 0735-0015

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:113313

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