351 citations · 1.2k across the 48 of their papers we have counts for
6 papers · 1 filter
Learning Latent Causal Dynamics
Weiran Yao, Guangyi Chen, Kun Zhang
One critical challenge of time-series modeling is how to learn and quickly correct the model under unknown distribution shifts. In this work, we propose a principled framework, cal…
Learning Temporally Causal Latent Processes from General Temporal Data
Weiran Yao, Yuewen Sun, Alex Ho +2
Our goal is to recover time-delayed latent causal variables and identify their relations from measured temporal data. Estimating causally-related latent variables from observations…
Likelihood-Free Overcomplete ICA and Applications in Causal Discovery
Chenwei Ding, Mingming Gong, Kun Zhang +1
Causal discovery witnessed significant progress over the past decades. In particular, many recent causal discovery methods make use of independent, non-Gaussian noise to achieve id…
Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA
Ricardo Pio Monti, Kun Zhang, Aapo Hyvarinen
We consider the problem of inferring causal relationships between two or more passively observed variables. While the problem of such causal discovery has been extensively studied…
A New Measure of Conditional Dependence
Jalal Etesami, Kun Zhang, Negar Kiyavash
Measuring conditional dependencies among the variables of a network is of great interest to many disciplines. This paper studies some shortcomings of the existing dependency measur…
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components
Philipp Geiger, Kun Zhang, Mingming Gong +2
A widely applied approach to causal inference from a non-experimental time series , often referred to as "(linear) Granger causal analysis", is to regress present on past and in…