Estimation of network treatment effects with non-ignorable missing confounders

Sun Zhaohan (), Zhu Yeying () and Dubin Joel A. ()
Additional contact information
Sun Zhaohan: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada
Zhu Yeying: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada
Dubin Joel A.: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada
Authors registered in the RePEc Author Service: Myoung-jae Lee

Journal of Causal Inference, 2024, vol. 12, issue 1, 16

Abstract: In causal inference, interference takes place when the intervention on one unit affects the outcome of other units. Most of the previous methods for estimating network causal effects assume that the covariate information is complete, which may lead to biased estimates when missingness exists. In this study, we consider the partial and direct interference setting. Specifically, the whole population can be divided into different clusters. Within each cluster, the outcome of each unit is dependent on the intervention received by other units, but not dependent on the confounders or outcomes of other units within the same cluster or of those in different clusters. We also assume that the confounders are subject to non-ignorable missingness, and a confounder is considered as missing if any component of it is missing. We propose three consistent estimators for the direct, indirect, total, and overall effect of the intervention on the outcome, and derive the asymptotic results accordingly. A comprehensive study is carried out as well to investigate the finite sample properties of the proposed estimators. We illustrate the proposed methods by analyzing the dataset collected from an acid rain program, which was launched to reduce air pollution in the United States by encouraging the scrubber’s installation on power plants, where the records of some operating characteristics of the power generating facilities are subject to missingness.

Keywords: causal inference; missing data; partial interference; air pollution (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/jci-2022-0079 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:12:y:2024:i:1:p:16:n:1001

DOI: 10.1515/jci-2022-0079

Access Statistics for this article

Journal of Causal Inference is currently edited by Elias Bareinboim, Jin Tian and Iván Díaz

More articles in Journal of Causal Inference from De Gruyter
Bibliographic data for series maintained by Peter Golla ().