Optimal and Non-Discriminative Rehabilitation Program Design for Opioid Addiction Among Homeless Youth

This paper presents CORTA, a software agent that designs personalized rehabilitation programs for homeless youth suffering from opioid addiction. Many rehabilitation centers treat opioid addiction in homeless youth by prescribing rehabilitation programs that are tailored to the underlying causes of addiction. To date, rehabilitation centers have relied on ad-hoc assessments and unprincipled heuristics to deliver rehabilitation programs to homeless youth suffering from opioid addiction, which greatly undermines the effectiveness of the delivered programs. CORTA addresses these challenges via three novel contributions. First, CORTA utilizes a first-of-its-kind real-world dataset collected from ~1400 homeless youth to build causal inference models which predict the likelihood of opioid addiction among these youth. Second, utilizing counterfactual predictions generated by our causal inference models, CORTA solves novel optimization formulations to assign appropriate rehabilitation programs to the correct set of homeless youth in order to minimize the expected number of homeless youth suffering from opioid addiction. Third, we provide a rigorous experimental analysis of CORTA along different dimensions, e.g., importance of causal modeling, importance of optimization, and impact of incorporating fairness considerations, etc. Our simulation results show that CORTA outperforms baselines by ~110% in minimizing the number of homeless youth suffering from opioid addiction.

Reference: Amulya Yadav, Roopali Singh, Nikolas Siapoutis, Anamika Barman-Adhikari, Yu Liang, "Optimal and Non-Discriminative Rehabilitation Program Design for Opioid Addiction Among Homeless Youth," Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Special track on AI for CompSust and Human well-being. Pages 4389-4395. https://doi.org/10.24963/ijcai.2020/605

Copyright 2020, IJCAI.

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Work Title Optimal and Non-Discriminative Rehabilitation Program Design for Opioid Addiction Among Homeless Youth
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Open Access
Creators
  1. Amulya Yadav
  2. Roopali Singh
  3. Nikolas Siapoutis
  4. Anamika Barman-Adhikari
  5. Yu Liang
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. International Joint Conferences on Artificial Intelligence Organization
Publication Date July 2020
Publisher Identifier (DOI)
  1. 10.24963/ijcai.2020/605
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  1. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Deposited November 11, 2022

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  • Added 0605.pdf
  • Added Creator Amulya Yadav
  • Added Creator Roopali Singh
  • Added Creator Nikolas Siapoutis
  • Added Creator Anamika Barman-Adhikari
  • Added Creator Yu Liang
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  • Updated Work Title, Description, Publisher's Statement Show Changes
    Work Title
    • Optimal and Non-Discriminative Rehabilitation Program Design for Opioid Addiction Among Homeless Youth.
    • Optimal and Non-Discriminative Rehabilitation Program Design for Opioid Addiction Among Homeless Youth
    Description
    • <jats:p>This paper presents CORTA, a software agent that designs personalized rehabilitation programs for homeless youth suffering from opioid addiction. Many rehabilitation centers treat opioid addiction in homeless youth by prescribing rehabilitation programs that are tailored to the underlying causes of addiction. To date, rehabilitation centers have relied on ad-hoc assessments and unprincipled heuristics to deliver rehabilitation programs to homeless youth suffering from opioid addiction, which greatly undermines the effectiveness of the delivered programs. CORTA addresses these challenges via three novel contributions. First, CORTA utilizes a first-of-its-kind real-world dataset collected from ~1400 homeless youth to build causal inference models which predict the likelihood of opioid addiction among these youth. Second, utilizing counterfactual predictions generated by our causal inference models, CORTA solves novel optimization formulations to assign appropriate rehabilitation programs to the correct set of homeless youth in order to minimize the expected number of homeless youth suffering from opioid addiction. Third, we provide a rigorous experimental analysis of CORTA along different dimensions, e.g., importance of causal modeling, importance of optimization, and impact of incorporating fairness considerations, etc. Our simulation results show that CORTA outperforms baselines by ~110% in minimizing the number of homeless youth suffering from opioid addiction.</jats:p>
    • This paper presents CORTA, a software agent that designs personalized rehabilitation programs for homeless youth suffering from opioid addiction. Many rehabilitation centers treat opioid addiction in homeless youth by prescribing rehabilitation programs that are tailored to the underlying causes of addiction. To date, rehabilitation centers have relied on ad-hoc assessments and unprincipled heuristics to deliver rehabilitation programs to homeless youth suffering from opioid addiction, which greatly undermines the effectiveness of the delivered programs. CORTA addresses these challenges via three novel contributions. First, CORTA utilizes a first-of-its-kind real-world dataset collected from ~1400 homeless youth to build causal inference models which predict the likelihood of opioid addiction among these youth. Second, utilizing counterfactual predictions generated by our causal inference models, CORTA solves novel optimization formulations to assign appropriate rehabilitation programs to the correct set of homeless youth in order to minimize the expected number of homeless youth suffering from opioid addiction. Third, we provide a rigorous experimental analysis of CORTA along different dimensions, e.g., importance of causal modeling, importance of optimization, and impact of incorporating fairness considerations, etc. Our simulation results show that CORTA outperforms baselines by ~110% in minimizing the number of homeless youth suffering from opioid addiction.
    Publisher's Statement
    • Reference: Amulya Yadav, Roopali Singh, Nikolas Siapoutis, Anamika Barman-Adhikari, Yu Liang, "Optimal and Non-Discriminative Rehabilitation Program Design for Opioid Addiction Among Homeless Youth," Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Special track on AI for CompSust and Human well-being. Pages 4389-4395. https://doi.org/10.24963/ijcai.2020/605
    • Copyright 2020, [IJCAI](ijcai.org).
    • Reference: Amulya Yadav, Roopali Singh, Nikolas Siapoutis, Anamika Barman-Adhikari, Yu Liang, "Optimal and Non-Discriminative Rehabilitation Program Design for Opioid Addiction Among Homeless Youth," Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Special track on AI for CompSust and Human well-being. Pages 4389-4395. https://doi.org/10.24963/ijcai.2020/605
    • Copyright 2020, [IJCAI](https://ijcai.org).
  • Updated