[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125972-en":3,"doc-seo-125972-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125972,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Causal Machine Learning for Cost-Effective Allocation of Development Aid","The paper targets the need for cost-effective allocation of development aid to achieve the UN Sustainable Development Goals by 2030, focusing on heterogeneous effects of aid disbursements under limited budgets and real-world allocation constraints. A causal machine learning framework is proposed to estimate individualized treatment–response curves and counterfactual outcomes. The method combines a balancing autoencoder, a counterfactual generator for continuous treatments, and an inference model for heterogeneous response prediction. Experiments on HIV/AIDS-focused official development aid across 105 countries reduce new infections by up to 3.3% versus current practice.","Causal Machine Learning for Cost-Effective Allocation of  \nDevelopment Aid  \narXiv :2401 . 16986v3 [ stat .ML] 15 Jun 2024  \nMilan Kuzmanovic  \n[mkuzma96@gmail.com](mkuzma96@gmail.com)[ ](mkuzma96@gmail.com)ETH Zurich Zurich, Switzerland  \nTobias Hatt[hatt.tob@gmail.com](hatt.tob@gmail.com)  \nETH Zurich Zurich, Switzerland  \nDennis Frauen  \n[frauen@lmu.de](frauen@lmu.de)  \nMunich Center for Machine Learning & LMU Munich Munich, Germany  \nStefan Feuerriegel  \n[feuerriegel@lmu.de](feuerriegel@lmu.de)  \nMunich Center for Machine Learning & LMU Munich Munich, Germany  \nABSTRACT  \nThe Sustainable Development Goals (SDGs) of the United Nations provide a blueprint of a better future by “leaving no one behind”, and, to achieve the SDGs by 2030, poor countries require immense volumes of development aid. In this paper, we develop a causal machine learning framework for predicting heterogeneous treatment effects of aid disbursements to inform effective aid allocation. Specifically, our framework comprises three components: (i) a balancing autoencoder that uses representation learning to embed high-dimensional country characteristics while addressing treatment selection bias; (ii) a counterfactual generator to compute counterfactual outcomes for varying aid volumes to address small sample-size settings; and (iii) an inference model that is used to predict heterogeneous treatment–response curves. We demonstrate the effectiveness of our framework using data with official development aid earmarked to end HIV/AIDS in 105 countries, amounting to more than USD 5 . 2 billion. For this, we first show that our framework successfully computes heterogeneous treatment–response curves using semi-synthetic data. Then, we demonstrate our framework using real-world HIV data. Our framework points to large opportunities for a more effective aid allocation, suggesting that the total number of new HIV infections could be reduced by up to 3.3%(∼50,000 cases) compared to the current allocation practice.  \nCCS CONCEPTS  \n• Applied computing → Life and medical sciences; • Information systems → Data mining; • Computing methodologies → Causal reasoning and diagnostics.  \nKEYWORDS  \ncausal machine learning, heterogeneous treatment effects, treatment effect estimation, development aid, medicine  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission [and/or a fee. Request permissions from permissions@acm.org](and/or a fee. Request permissions from permissions@acm.org).  \nKDD’24, August 25–29, 2024, Barcelona, Spain  \n© 2024 Copyright held by the owner/author(s) . Publication rights licensed to ACM. ACM ISBN 979-8-4007-0490-1/24/08  \n[https://doi.org/10.1145/3637528.3671551](https://doi.org/10.1145/3637528.3671551)  \nACM Reference Format:  \nMilan Kuzmanovic, Dennis Frauen, Tobias Hatt, and Stefan Feuerriegel. 2024. Causal Machine Learning for Cost-Effective Allocation of Development Aid. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’24), August 25–29, 2024, Barcelona, Spain. ACM, New York, NY, USA, 21 pages. [https://doi.org/10.1145/3637528.3671551](https://doi.org/10.1145/3637528.3671551)  \n1 INTRODUCTION  \nThe Sustainable Development Goals (SDGs) by the United Nations define a global framework to achieve a better and more sustainable future for the people and the planet [48] . The SDGs list various targets that should be met by 2030 (e.g., ending the HIV/AIDS epidemic, eliminating hunger) . Here, a central principle of the SDGs is that of “leaving no one b","cbCaiuCNGcZ8HVDk","https://ap.wps.com/l/cbCaiuCNGcZ8HVDk","pdf",1911989,7,1,21,"English","en",105,"# Abstract\n## Method Components\n## Evaluation on Semi-Synthetic and Real-World HIV Data\n## Impact on HIV/AIDS Outcomes\n## Introduction","[{\"question\":\"What problem does the paper address in development aid allocation?\",\"answer\":\"It addresses inefficient aid allocation caused by reliance on human judgment and limited budgets, by predicting how aid disbursements affect SDG outcomes with attention to heterogeneous effects.\"},{\"question\":\"How does the proposed CG-CT framework handle continuous aid treatments and confounding?\",\"answer\":\"CG-CT predicts heterogeneous treatment effects for aid disbursements modeled as continuous treatments while controlling for confounders and specifically addressing treatment selection bias and small sample-size settings.\"},{\"question\":\"What data and outcome setting are used to demonstrate the framework?\",\"answer\":\"The framework is evaluated using official development aid earmarked to end HIV/AIDS in 105 countries, and results are shown on both semi-synthetic data and real-world HIV data.\"}]","Causal Machine Learning for Cost-Effective Allocation of Development Aid | 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problem does the paper address in development aid allocation?","Question",{"text":77,"@type":78},"It addresses inefficient aid allocation caused by reliance on human judgment and limited budgets, by predicting how aid disbursements affect SDG outcomes with attention to heterogeneous effects.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does the proposed CG-CT framework handle continuous aid treatments and confounding?",{"text":82,"@type":78},"CG-CT predicts heterogeneous treatment effects for aid disbursements modeled as continuous treatments while controlling for confounders and specifically addressing treatment selection bias and small sample-size settings.",{"name":84,"@type":75,"acceptedAnswer":85},"What data and outcome setting are used to demonstrate the framework?",{"text":86,"@type":78},"The framework is evaluated using official development aid earmarked to end HIV/AIDS in 105 countries, and results are shown on both semi-synthetic data and real-world HIV 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