Causal machine-learning is about predicting the net-effect (true-lift) of treatments. Given the data of a treatment group and a control group, it is similar to a standard supervised-learning problem. Unfortunately, there is no similarly well-defined loss function due to the lack of point-wise true values in the data. Many advances in modern machine-learning are not directly applicable due to the absence of such loss function.
We propose a novel method to define a loss function in this context, which is equal to mean-square-error (MSE) in a standard regression problem. Our loss function is universally applicable, thus providing a general standard to evaluate the quality of any model/strategy that predicts the true-lift. We demonstrate that despite its novel definition, one can still perform gradient descent directly on this loss function to find the best fit. This leads to a new way to train any parameter-based model, such as deep neural networks, to solve causal machine-learning problems without going through the meta-learner strategy.
Socioeconomic disparities and COVID-19: the causal connections
Auburn University（奥本大学）；DESY（德国电子同步加速器研究所）；Humboldt-Universität zu Berlin（柏林洪堡大学）；BASIS Independent Silicon Valley（贝赛思独立学校硅谷校区）；Stanford Online High School（斯坦福在线高中）；NTNU（挪威科技大学）；SimulaMet（SimulaMet研究所）
Department of Computer Science, University of Bath, Bath, UK（英国巴斯大学计算机科学系）；TAU, LISN, INRIA Saclay, France（法国萨克利实验室）；INSA Rouen Normandy, Normandy University, LITIS, Rouen, France（法国诺曼底大学）
Causal machine learning for predicting treatment outcomes
LMU Munich（慕尼黑大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；University of Cambridge（剑桥大学）；TU Munich（慕尼黑工业大学）；Helmholtz Munich（慕尼黑亥姆霍兹中心）；Harvard Medical School（哈佛医学院）；Cambridge Centre for AI in Medicine（剑桥人工智能医学中心）；The Alan Turing Institute（阿兰·图灵研究所）
Causal Explainability of Machine Learning in Heart Failure Prediction from Electronic Health Records
Department of Computer Science（计算机科学系）；Tennessee State University（田纳西州立大学）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Mathematics and Statistics（数学与统计学系）；Old Dominion University（老奥尔巴尼大学）
Causal Machine Learning: A Survey and Open Problems
AI Centre, Department of Computer Science, UCL（人工智能中心，计算机科学系，伦敦大学学院）；Department of Computer Science, University of Hong Kong（计算机科学系，香港大学）；AI Centre, Department of Statistical Science, UCL（人工智能中心，统计科学系，伦敦大学学院）
What if? Causal Machine Learning in Supply Chain Risk Management
Poznań University of Economics and Business（波兹南经济与商业大学）；University of Huddersfield（哈德斯菲尔德大学）；University of Cambridge（剑桥大学）；Alan Turing Institute（阿兰·图灵研究所）
Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
School of Business and Economics, University of Tübingen（蒂宾根大学商学院与经济学院）；Hertie Institute for AI in Brain Health, University of Tübingen（蒂宾根大学赫蒂脑健康人工智能研究所）
Causal Machine Learning Is Not a Panacea: A Roadmap for Observational Causal Inference in Health
Division of Computer Science and Engineering, University of Michigan（密歇根大学计算机科学与工程系）；Department of Electrical and Electronic Engineering, Imperial College London（伦敦帝国理工学院电子与电气工程系）；Courant Institute of Mathematical Sciences, New York University（纽约大学Courant数学科学研究所）；Center for Data Science, New York University（纽约大学数据科学中心）；Department of Mathematics & Statistics, Elon University（埃洛伊大学数学与统计学系）；Department of Ophthalmology, Cambridge University Hospitals（剑桥大学医院眼科部）；Department of Biomedical Informatics, Columbia University（哥伦比亚大学生物医学信息学系）；School of Engineering and Applied Science, Harvard University（哈佛大学工程与应用科学学院）；Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology（麻省理工学院医学工程与科学研究所计算生理学实验室）；Department of Medicine, Beth Israel Deaconess Medical Center（贝斯以色列德aconess医疗中心医学部）；Department of Biostatistics, Harvard T.H. Chan School of Public Health（哈佛T.H. Chan公共卫生学院生物统计学系）
Causality in the human niche: lessons for machine learning
Dept. of Computer Science, Rochester Institute of Technology（计算机科学系，罗切斯特理工学院）；Cognitive Science PhD Program, Rochester Institute of Technology（认知科学博士项目，罗切斯特理工学院）；Center for Vision Science, University of Rochester（视觉科学中心，罗切斯特大学）；Dept. of Neurobiology, University of Pennsylvania（神经生物学系，宾夕法尼亚大学）；CIFAR LMB
Max-Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；ETH Zurich（苏黎世联邦理工学院）；Google Research Amsterdam（谷歌研究院阿姆斯特丹分部）；Mila（米拉研究所）；University of Montreal（蒙特利尔大学）
REX: Causal discovery based on machine learning and explainability techniques
BBVA Madrid, Spain（西班牙BBVA马德里）；Instituto de Ciencia de Datos e Inteligencia Artificil (DATAI)（数据与人工智能研究所）；University of Navarra（纳瓦拉大学）；Tecnun, University of Navarra（纳瓦拉大学技术学院）
Impact, Causation and Prediction of Socio-Academic and Economic Factors in Exam-centric Student Evaluation Measures using Machine Learning and Causal Analysis
Institute of Information Technology（信息科技研究所）；Jahangirnagar University（贾汗吉尔纳加尔大学）；University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；Erik Jonsson School of Engineering and Computer Science（埃里克·乔纳森工程与计算机科学学院）；University of Texas（德克萨斯大学）
