109 citations · 346 across the 39 of their papers we have counts for
10 papers · 1 filter
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…
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…
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View
Haoyue Dai, Ignavier Ng, Gongxu Luo +3
Gene regulatory network inference (GRNI) is a challenging problem, particularly owing to the presence of zeros in single-cell RNA sequencing data: some are biological zeros represe…
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…
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…
Causal Representation Learning from Multiple Distributions: A General Setting
Kun Zhang, Shaoan Xie, Ignavier Ng +1
In many problems, the measured variables (e.g., image pixels) are just mathematical functions of the latent causal variables (e.g., the underlying concepts or objects). For the pur…