Estimating Density Ratio of Marginals to Joint: Applications to Causal Inference

Yukitoshi Matsushita, Taisuke Otsu, Keisuke Takahata

研究成果: Article査読

抄録

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.

本文言語English
ジャーナルJournal of Business and Economic Statistics
DOI
出版ステータスAccepted/In press - 2022

ASJC Scopus subject areas

  • 統計学および確率
  • 社会科学(その他)
  • 経済学、計量経済学
  • 統計学、確率および不確実性

フィンガープリント

「Estimating Density Ratio of Marginals to Joint: Applications to Causal Inference」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル