Quantifying cause and effect relationships is an important problem in many domains. The gold standard solution is to conduct a randomised controlled trial. However, in many situations such trials cannot be performed. In the absence of such trials, many methods have been devised to quantify the causal impact of an intervention from observational data given certain assumptions. One widely used method are synthetic control models. While identifiability of the causal estimand in such models has been obtained from a range of assumptions, it is widely and implicitly assumed that the underlying assumptions are satisfied for all time periods both pre- and post-intervention. This is a strong assumption, as synthetic control models can only be learned in pre-intervention period. In this paper we address this challenge, and prove identifiability can be obtained without the need for this assumption, by showing it follows from the principle of invariant causal mechanisms. Moreover, for the first time, we formulate and study synthetic control models in Pearl's structural causal model framework. Importantly, we provide a general framework for sensitivity analysis of synthetic control causal inference to violations of the assumptions underlying non-parametric identifiability. We end by providing an empirical demonstration of our sensitivity analysis framework on simulated and real data in the widely-used linear synthetic control framework.
On the Parameter Identifiability of Partially Observed Linear Causal Models
Carnegie Mellon University（卡内基梅隆大学）；University of California San Diego（加州大学圣迭戈分校）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；KDDI Research（KDDI研究所）
Identifiable Latent Polynomial Causal Models Through the Lens of Change
Australian Institute for Machine Learning, The University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；School of Computer Science and Engineering, The University of New South Wales（新南威尔士大学计算机科学与工程学院）；School of Mathematics and Statistics, The University of Melbourne（墨尔本大学数学与统计学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Halicioğlu Data Science Institute (HDSI), University of California San Diego（加州大学圣地亚哥分校哈利西奥格鲁数据科学研究所）；Carnegie Mellon University（卡内基梅隆大学）
Identification of Nonlinear Latent Hierarchical Models
Carnegie Mellon University（卡内基梅隆大学）；University of California San Diego（加州大学圣迭戈分校）；Beijing Technology and Business University（北京工商大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Demystifying amortized causal discovery with transformers
Institute of Science and Technology Austria（奥地利科学与技术研究所）；Chan Zuckerberg Initiative（查纳·泽克伯格倡议）；Mila - Quebec AI Institute and Université de Montréal（蒙特利尔AI研究所和蒙特利尔大学）
Synthetic Potential Outcomes and Causal Mixture Identifiability
Eric and Wendy Schmidt Center（埃里克和温迪·施密特中心）；Broad Institute of MIT and Harvard（麻省理工学院与哈佛大学布罗德研究所）；Laboratory for Information and Decision Systems（信息与决策系统实验室）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）
Responsible AI Research Centre（负责任人工智能研究中心）；Australian Institute for Machine Learning（澳大利亚机器学习研究所）；School of Computer Science and Engineering（计算机科学与工程学院）；School of Mathematics and Statistics（数学与统计学学院）；Halicioğlu Data Science Institute（Halicioğlu数据科学研究所）；Department of Philosophy（哲学系）
Consistent Empirical Bayes estimation of the mean of a mixing distribution without identifiability assumption. With applications to treatment of non-response
A Robbins-Monro algorithm for non-parametric estimation of NAR process with Markov-Switching: asymptotic normality
Aix-Marseille University（艾克斯-马赛大学）；Escuela de Matemáticas, Facultad de Ciencias, Universidad Central de Venezuela（委内瑞拉中央大学理学院数学系）；Departamento de Matemáticas, FACYT, Universidad de Carabobo（卡拉沃博大学FACYT数学系）
An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis
Carnegie Mellon University（卡内基梅隆大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；International Max Planck Research School for Intelligent Systems (IMPRS-IS)（马克斯·普朗克智能系统国际研究学校（IMPRS-IS））；European Laboratory for Learning and Intelligent Systems (ELLIS)（欧洲学习与智能系统实验室（ELLIS））
Towards Identifiability of Hierarchical Temporal Causal Representation Learning
Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Guangdong University of Technology（广东技术大学）；University of California San Diego（加州大学圣地亚哥分校）
