24 citations · 69 across the 15 of their papers we have counts for
6 papers · 1 filter
CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process
Guangyi Chen, Yifan Shen, Zhenhao Chen +5
Identifying the underlying time-delayed latent causal processes in sequential data is vital for grasping temporal dynamics and making downstream reasoning. While some recent method…
Temporally Disentangled Representation Learning under Unknown Nonstationarity
Xiangchen Song, Weiran Yao, Yewen Fan +5
In unsupervised causal representation learning for sequential data with time-delayed latent causal influences, strong identifiability results for the disentanglement of causally-re…
Partial Identifiability for Domain Adaptation
Lingjing Kong, Shaoan Xie, Weiran Yao +5
Unsupervised domain adaptation is critical to many real-world applications where label information is unavailable in the target domain. In general, without further assumptions, the…
On the Unlikelihood of D-Separation
Itai Feigenbaum, Huan Wang, Shelby Heinecke +4
Causal discovery aims to recover a causal graph from data generated by it; constraint based methods do so by searching for a d-separating conditioning set of nodes in the graph via…
Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data
Devansh Arpit, Matthew Fernandez, Itai Feigenbaum +11
We introduce the Salesforce CausalAI Library, an open-source library for causal analysis using observational data. It supports causal discovery and causal inference for tabular and…
Temporally Disentangled Representation Learning
Weiran Yao, Guangyi Chen, Kun Zhang
Recently in the field of unsupervised representation learning, strong identifiability results for disentanglement of causally-related latent variables have been established by expl…