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

Matsushita, Yukitoshi, Otsu, Taisuke ORCID: 0000-0002-2307-143X and Takahata, Keisuke (2023) Estimating density ratio of marginals to joint: applications to causal inference. Journal of Business and Economic Statistics, 41 (2). 467 - 481. ISSN 0735-0015

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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.

Item Type:	Article
Official URL:	https://www.tandfonline.com/journals/ubes20
Additional Information:	© 2022 The Authors
Divisions:	Economics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	04 Jan 2022 18:57
Last Modified:	04 Nov 2024 07:18
URI:	http://eprints.lse.ac.uk/id/eprint/113313

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