research-article

Bounds on Direct and Indirect Effects of Treatment on a Continuous Endpoint

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Zhi GengAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 7, Issue 2

Article No.: 20, Pages 1 - 18

https://doi.org/10.1145/2668134

Published: 17 December 2015 Publication History

Abstract

Direct effect of a treatment variable on an endpoint variable and indirect effect through a mediate variable are important concepts for understanding a causal mechanism. However, the randomized assignment of treatment is not sufficient for identifying the direct and indirect effects, and extra assumptions and conditions are required, such as the sequential ignorability assumption without unobserved confounders or the sequential potential ignorability assumption. But these assumptions may not be credible in many applications. In this article, we consider the bounds on controlled direct effect, natural direct effect, and natural indirect effect without these extra assumptions. Cai et al. [2008] presented the bounds for the case of a binary endpoint, and we extend their results to the general case for an arbitrary endpoint.

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Index Terms

Bounds on Direct and Indirect Effects of Treatment on a Continuous Endpoint
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 7, Issue 2

Special Issue on Causal Discovery and Inference

January 2016

270 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2850424

Editor:
Yu Zheng
Microsoft Research, China

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Copyright © 2015 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 December 2015

Accepted: 01 September 2014

Revised: 01 June 2014

Received: 01 March 2014

Published in TIST Volume 7, Issue 2

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Cited By

Wu FLyu FRen JYang PQian KGao SZhang Y(2023)Characterizing Internet Card User Portraits for Efficient Churn Prediction Model DesignIEEE Transactions on Mobile Computing10.1109/TMC.2023.324120623:2(1735-1752)Online publication date: 31-Jan-2023
https://dl.acm.org/doi/10.1109/TMC.2023.3241206
Qiao YZhang JHe QGu YWu JZhang LWang C(2022)Truthful Profit Maximization Mechanisms for Mobile CrowdsourcingWireless Communications & Mobile Computing10.1155/2022/88599052022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8859905
Truong NDinh LStein STran-Thanh LJennings N(2022)Efficient and adaptive incentive selection for crowdsourcing contestsApplied Intelligence10.1007/s10489-022-03593-253:8(9204-9234)Online publication date: 6-Aug-2022
https://dl.acm.org/doi/10.1007/s10489-022-03593-2
Li YChang LLi LBao XGu T(2021)Key Research Issues and Related Technologies in Crowdsourcing Data CollectionWireless Communications & Mobile Computing10.1155/2021/87458972021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/8745897
Samanta RGhosh SDas S(2021)SWill-TAC: Skill-oriented Dynamic Task Allocation with Willingness for Complex Job in Crowdsourcing2021 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOBECOM46510.2021.9685885(1-6)Online publication date: 7-Dec-2021
https://dl.acm.org/doi/10.1109/GLOBECOM46510.2021.9685885
Liu CWang HWang YSun D(2019)Utility-Based Location Distribution Reverse Auction Incentive Mechanism for Mobile Crowd Sensing NetworkAlgorithms and Architectures for Parallel Processing10.1007/978-3-030-38961-1_11(116-127)Online publication date: 9-Dec-2019
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