Latent Causal

Latent causal representation learning aims to uncover hidden causal variables and their relationships from high-dimensional observational data, enabling causal inference and improved prediction in various domains. Current research focuses on developing methods that address challenges like non-linearity, partial observability, and unknown interventions, often employing techniques such as autoencoders, variational inference, and graph neural networks to learn disentangled representations and identify causal structures. These advancements are significant for improving the interpretability and robustness of machine learning models, and have implications across diverse fields including healthcare, ecology, and reinforcement learning.

Papers

November 29, 2022

Linear Causal Disentanglement via Interventions
Chandler Squires, Anna Seigal, Salil Bhate, Caroline Uhler
Latent Variable Causal Model Latent Causal Health Intervention Causal Disentanglement Latent Linear

October 24, 2022

Temporally Disentangled Representation Learning
Weiran Yao, Guangyi Chen, Kun Zhang
Latent Causal Parametric Causal

October 1, 2022

Latent Hierarchical Causal Structure Discovery with Rank Constraints
Biwei Huang, Charles Jia Han Low, Feng Xie, Clark Glymour, Kun Zhang
Latent Variable Latent Causal Latent Confounders Hierarchical Graph Latent Hierarchy

August 30, 2022

June 24, 2022

Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings
Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
Reinforcement Learning Markov Decision Process Partially Observable Markov Decision Process Latent Causal Action Value Function

March 30, 2022

Weakly supervised causal representation learning
Johann Brehmer, Pim de Haan, Phillip Lippe, Taco Cohen
Variational Autoencoders Causal Structure Causal Representation Learning Latent Causal

March 29, 2022

OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks
Wanyu Lin, Hao Lan, Hao Wang, Baochun Li
Causal Graph Explanation Generation Latent Causal Subgraph Explanation Causal Explanation Interpretable Graph Neural Network Causal Hierarchy

March 21, 2022

Learning latent causal relationships in multiple time series
Jacek P. Dmochowski
Causal Structure Causal Relationship Causal Relation Latent Causal Granger Causality

February 10, 2022

Learning Latent Causal Dynamics
Weiran Yao, Guangyi Chen, Kun Zhang
Data Dimensionality Latent Causal