26 citations · 207 across the 48 of their papers we have counts for
40 papers · 1 filter
Score-Based Causal Discovery of Latent Variable Causal Models
Ignavier Ng, Xinshuai Dong, Haoyue Dai +3
Identifying latent variables and the causal structure involving them is essential across various scientific fields. While many existing works fall under the category of constraint-…
Causal Representation Meets Stochastic Modeling under Generic Geometry
Jiaxu Ren, Yixin Wang, Biwei Huang
Learning meaningful causal representations from observations has emerged as a crucial task for facilitating machine learning applications and driving scientific discoveries in fiel…
A Fast Kernel-based Conditional Independence test with Application to Causal Discovery
Oliver Schacht, Biwei Huang
Kernel-based conditional independence (KCI) testing is a powerful nonparametric method commonly employed in causal discovery tasks. Despite its flexibility and statistical reliabil…
Analytic DAG Constraints for Differentiable DAG Learning
Zhen Zhang, Ignavier Ng, Dong Gong +6
Recovering the underlying Directed Acyclic Graph (DAG) structures from observational data presents a formidable challenge, partly due to the combinatorial nature of the DAG-constra…
I Predict Therefore I Am: Is Next Token Prediction Enough to Learn Human-Interpretable Concepts from Data?
Yuhang Liu, Dong Gong, Yichao Cai +6
Recent empirical evidence shows that LLM representations encode human-interpretable concepts. Nevertheless, the mechanisms by which these representations emerge remain largely unex…
Learning General Causal Structures with Hidden Dynamic Process for Climate Analysis
Minghao Fu, Biwei Huang, Zijian Li +5
Understanding climate dynamics requires going beyond correlations in observational data to uncover the underlying causal process. Latent drivers such as atmospheric processes play…
Differentiable Causal Discovery For Latent Hierarchical Causal Models
Parjanya Prashant, Ignavier Ng, Kun Zhang +1
Discovering causal structures with latent variables from observational data is a fundamental challenge in causal discovery. Existing methods often rely on constraint-based, iterati…
Revisiting Differentiable Structure Learning: Inconsistency of Penalty and Beyond
Kaifeng Jin, Ignavier Ng, Kun Zhang +1
Recent advances in differentiable structure learning have framed the combinatorial problem of learning directed acyclic graphs as a continuous optimization problem. Various aspects…
A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery
Yingyu Lin, Yuxing Huang, Wenqin Liu +6
Real-world data often violates the equal-variance assumption (homoscedasticity), making it essential to account for heteroscedastic noise in causal discovery. In this work, we expl…
Rethinking State Disentanglement in Causal Reinforcement Learning
Haiyao Cao, Zhen Zhang, Panpan Cai +7
One of the significant challenges in reinforcement learning (RL) when dealing with noise is estimating latent states from observations. Causality provides rigorous theoretical supp…
An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series
Qiang Huang, Chuizheng Meng, Defu Cao +3
Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the miti…
Reinforcement Learning for Causal Discovery without Acyclicity Constraints
Bao Duong, Hung Le, Biwei Huang +1
Recently, reinforcement learning (RL) has proved a promising alternative for conventional local heuristics in score-based approaches to learning directed acyclic causal graphs (DAG…
Optimal Kernel Choice for Score Function-based Causal Discovery
Wenjie Wang, Biwei Huang, Feng Liu +4
Score-based methods have demonstrated their effectiveness in discovering causal relationships by scoring different causal structures based on their goodness of fit to the data. Rec…
On the Parameter Identifiability of Partially Observed Linear Causal Models
Xinshuai Dong, Ignavier Ng, Biwei Huang +5
Linear causal models are important tools for modeling causal dependencies and yet in practice, only a subset of the variables can be observed. In this paper, we examine the paramet…
Learning Discrete Concepts in Latent Hierarchical Models
Lingjing Kong, Guangyi Chen, Biwei Huang +3
Learning concepts from natural high-dimensional data (e.g., images) holds potential in building human-aligned and interpretable machine learning models. Despite its encouraging pro…
Towards Identifiable Latent Additive Noise Models
Yuhang Liu, Zhen Zhang, Dong Gong +6
Causal representation learning (CRL) offers the promise of uncovering the underlying causal model by which observed data was generated, but the practical applicability of existing…
Federated Causal Discovery from Heterogeneous Data
Loka Li, Ignavier Ng, Gongxu Luo +5
Conventional causal discovery methods rely on centralized data, which is inconsistent with the decentralized nature of data in many real-world situations. This discrepancy has moti…
Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning
Yuhang Liu, Zhen Zhang, Dong Gong +6
Directed Acyclic Graphs (DAGs) are a standard tool in causal modeling, but their suitability for capturing the complexity of large-scale multimodal data is questionable. In practic…
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
Xinshuai Dong, Biwei Huang, Ignavier Ng +6
Most existing causal discovery methods rely on the assumption of no latent confounders, limiting their applicability in solving real-life problems. In this paper, we introduce a no…
Identifiable Latent Polynomial Causal Models Through the Lens of Change
Yuhang Liu, Zhen Zhang, Dong Gong +5
Causal representation learning aims to unveil latent high-level causal representations from observed low-level data. One of its primary tasks is to provide reliable assurance of id…
Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables
Feng Xie, Biwei Huang, Zhengming Chen +4
We investigate the task of learning causal structure in the presence of latent variables, including locating latent variables and determining their quantity, and identifying causal…
Causal-learn: Causal Discovery in Python
Yujia Zheng, Biwei Huang, Wei Chen +6
Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering. We describe , an open-so…
Learning World Models with Identifiable Factorization
Yu-Ren Liu, Biwei Huang, Zhengmao Zhu +4
Extracting a stable and compact representation of the environment is crucial for efficient reinforcement learning in high-dimensional, noisy, and non-stationary environments. Diffe…
Identification of Nonlinear Latent Hierarchical Models
Lingjing Kong, Biwei Huang, Feng Xie +3
Identifying latent variables and causal structures from observational data is essential to many real-world applications involving biological data, medical data, and unstructured da…