Ignavier Ng

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-…

SEDGE: Structural Extrapolated Data Generation

Kun Zhang, Jiaqi Sun, Yiqing Li +3

This paper aims to address the challenge of data generation beyond the training data and proposes a framework for Structural Extrapolated Data GEneration (SEDGE) based on suitable…

Causal Representation Learning from General Environments under Nonparametric Mixing

Ignavier Ng, Shaoan Xie, Xinshuai Dong +2

Causal representation learning aims to recover the latent causal variables and their causal relations, typically represented by directed acyclic graphs (DAGs), from low-level obser…

A General Representation-Based Approach to Multi-Source Domain Adaptation

Ignavier Ng, Yan Li, Zijian Li +3

A central problem in unsupervised domain adaptation is determining what to transfer from labeled source domains to an unlabeled target domain. To handle high-dimensional observatio…

Latent Variable Causal Discovery under Selection Bias

Haoyue Dai, Yiwen Qiu, Ignavier Ng +3

Addressing selection bias in latent variable causal discovery is important yet underexplored, largely due to a lack of suitable statistical tools: While various tools beyond basic…

Higher-Order Causal Structure Learning with Additive Models

James Enouen, Yujia Zheng, Ignavier Ng +2

Causal structure learning has long been the central task of inferring causal insights from data. Despite the abundance of real-world processes exhibiting higher-order mechanisms, h…

Debiasing Reward Models by Representation Learning with Guarantees

Ignavier Ng, Patrick Blöbaum, Siddharth Bhandari +2

Recent alignment techniques, such as reinforcement learning from human feedback, have been widely adopted to align large language models with human preferences by learning and leve…

Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Models

Xinshuai Dong, Ignavier Ng, Haoyue Dai +4

Identifying the structure of a partially observed causal system is essential to various scientific fields. Recent advances have focused on constraint-based causal discovery to solv…

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…

When Selection Meets Intervention: Additional Complexities in Causal Discovery

Haoyue Dai, Ignavier Ng, Jianle Sun +5

We address the common yet often-overlooked selection bias in interventional studies, where subjects are selectively enrolled into experiments. For instance, participants in a drug…

Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning

Zijian Li, Shunxing Fan, Yujia Zheng +6

Disentangled representation learning aims to uncover latent variables underlying the observed data, and generally speaking, rather strong assumptions are needed to ensure identifia…

Permutation-Based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data

Xinshuai Dong, Ignavier Ng, Boyang Sun +6

Recent advances have shown that statistical tests for the rank of cross-covariance matrices play an important role in causal discovery. These rank tests include partial correlation…

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 $ℓ_{1}$ 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…

On the Identifiability of Sparse ICA without Assuming Non-Gaussianity

Ignavier Ng, Yujia Zheng, Xinshuai Dong +1

Independent component analysis (ICA) is a fundamental statistical tool used to reveal hidden generative processes from observed data. However, traditional ICA approaches struggle w…

Continual Learning of Nonlinear Independent Representations

Boyang Sun, Ignavier Ng, Guangyi Chen +3

Identifying the causal relations between interested variables plays a pivotal role in representation learning as it provides deep insights into the dataset. Identifiability, as the…

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…

Local Causal Discovery with Linear non-Gaussian Cyclic Models

Haoyue Dai, Ignavier Ng, Yujia Zheng +2

Local causal discovery is of great practical significance, as there are often situations where the discovery of the global causal structure is unnecessary, and the interest lies so…