Timezone: »
Traditional causal discovery methods mainly focus on estimating causal relations among measured variables, but in many real-world problems, such as questionnaire-based psychometric studies, measured variables are generated by latent variables that are causally related. Accordingly, this paper investigates the problem of discovering the hidden causal variables and estimating the causal structure, including both the causal relations among latent variables and those between latent and measured variables. We relax the frequently-used measurement assumption and allow the children of latent variables to be latent as well, and hence deal with a specific type of latent hierarchical causal structure. In particular, we define a minimal latent hierarchical structure and show that for linear non-Gaussian models with the minimal latent hierarchical structure, the whole structure is identifiable from only the measured variables. Moreover, we develop a principled method to identify the structure by testing for Generalized Independent Noise (GIN) conditions in specific ways. Experimental results on both synthetic and real-world data show the effectiveness of the proposed approach.
Author Information
Feng Xie (Peking University)
Biwei Huang (Carnegie Mellon University)
Zhengming Chen (Guangdong University of Technology)
Yangbo He (Peking University)
zhi geng (Peking University)
Kun Zhang (Carnegie Mellon University)
Related Events (a corresponding poster, oral, or spotlight)
-
2022 Spotlight: Identification of Linear Non-Gaussian Latent Hierarchical Structure »
Wed. Jul 20th 06:10 -- 06:15 PM Room Hall G
More from the Same Authors
-
2021 : Optimal transport for causal discovery »
Ruibo Tu · Kun Zhang · Hedvig Kjellström · Cheng Zhang -
2022 : Causal Balancing for Domain Generalization »
Xinyi Wang · Michael Saxon · Jiachen Li · Hongyang Zhang · Kun Zhang · William Wang -
2023 : Counterfactual Generation with Identifiability Guarantees »
Hanqi Yan · Lingjing Kong · Lin Gui · Yuejie Chi · Eric Xing · Yulan He · Kun Zhang -
2023 : Identification of Nonlinear Latent Hierarchical Causal Models »
Lingjing Kong · Biwei Huang · Feng Xie · Eric Xing · Yuejie Chi · Kun Zhang -
2023 : Advancing Counterfactual Inference through Quantile Regression »
Shaoan Xie · Biwei Huang · Bin Gu · Tongliang Liu · Kun Zhang -
2023 : Natural Counterfactuals With Necessary Backtracking »
Guangyuan Hao · Jiji Zhang · Hao Wang · Kun Zhang -
2023 : Natural Counterfactuals With Necessary Backtracking »
Guangyuan Hao · Jiji Zhang · Hao Wang · Kun Zhang -
2023 Poster: Trustworthy Policy Learning under the Counterfactual No-Harm Criterion »
Haoxuan Li · Chunyuan Zheng · Yixiao Cao · zhi geng · Yue Liu · Peng Wu -
2023 Poster: Identifiability of Label Noise Transition Matrix »
Yang Liu · Hao Cheng · Kun Zhang -
2023 Poster: Causal Discovery with Latent Confounders Based on Higher-Order Cumulants »
Ruichu Cai · Zhiyi Huang · Wei Chen · Zhifeng Hao · Kun Zhang -
2023 Poster: Feature Expansion for Graph Neural Networks »
Jiaqi Sun · Lin Zhang · Guangyi Chen · Peng XU · Kun Zhang · Yujiu Yang -
2023 Poster: Model Transferability with Responsive Decision Subjects »
Yatong Chen · Zeyu Tang · Kun Zhang · Yang Liu -
2023 Poster: Evolving Semantic Prototype Improves Generative Zero-Shot Learning »
Shiming Chen · Wenjin Hou · Ziming Hong · Xiaohan Ding · Yibing Song · Xinge You · Tongliang Liu · Kun Zhang -
2023 Poster: Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise? »
Yu Yao · Mingming Gong · Yuxuan Du · Jun Yu · Bo Han · Kun Zhang · Tongliang Liu -
2022 : Model Transferability With Responsive Decision Subjects »
Yang Liu · Yatong Chen · Zeyu Tang · Kun Zhang -
2022 Poster: Action-Sufficient State Representation Learning for Control with Structural Constraints »
Biwei Huang · Chaochao Lu · Liu Leqi · Jose Miguel Hernandez-Lobato · Clark Glymour · Bernhard Schölkopf · Kun Zhang -
2022 Spotlight: Action-Sufficient State Representation Learning for Control with Structural Constraints »
Biwei Huang · Chaochao Lu · Liu Leqi · Jose Miguel Hernandez-Lobato · Clark Glymour · Bernhard Schölkopf · Kun Zhang -
2022 Poster: Partial disentanglement for domain adaptation »
Lingjing Kong · Shaoan Xie · Weiran Yao · Yujia Zheng · Guangyi Chen · Petar Stojanov · Victor Akinwande · Kun Zhang -
2022 Spotlight: Partial disentanglement for domain adaptation »
Lingjing Kong · Shaoan Xie · Weiran Yao · Yujia Zheng · Guangyi Chen · Petar Stojanov · Victor Akinwande · Kun Zhang -
2020 Poster: Label-Noise Robust Domain Adaptation »
Xiyu Yu · Tongliang Liu · Mingming Gong · Kun Zhang · Kayhan Batmanghelich · Dacheng Tao -
2020 Poster: LTF: A Label Transformation Framework for Correcting Label Shift »
Jiaxian Guo · Mingming Gong · Tongliang Liu · Kun Zhang · Dacheng Tao -
2020 Poster: Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs »
AmirEmad Ghassami · Alan Yang · Negar Kiyavash · Kun Zhang -
2019 Poster: Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models »
Biwei Huang · Kun Zhang · Mingming Gong · Clark Glymour -
2019 Oral: Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models »
Biwei Huang · Kun Zhang · Mingming Gong · Clark Glymour -
2019 Poster: On Learning Invariant Representations for Domain Adaptation »
Han Zhao · Remi Tachet des Combes · Kun Zhang · Geoff Gordon -
2019 Oral: On Learning Invariant Representations for Domain Adaptation »
Han Zhao · Remi Tachet des Combes · Kun Zhang · Geoff Gordon