Causal-learn: Causal discovery in python
Causal discovery aims at revealing causal relations from observational data, which is a
fundamental task in science and engineering. We describe causal-learn, an open-source …
fundamental task in science and engineering. We describe causal-learn, an open-source …
Causal representation learning through higher-level information extraction
The large gap between the generalization level of state-of-the-art machine learning and
human learning systems calls for the development of artificial intelligence (AI) models that …
human learning systems calls for the development of artificial intelligence (AI) models that …
Causal representation learning from multiple distributions: A general setting
K Zhang, S ** of observational variables
A topic of great current interest is Causal Representation Learning (CRL), whose goal is to
learn a causal model for hidden features in a data-driven manner. Unfortunately, CRL is …
learn a causal model for hidden features in a data-driven manner. Unfortunately, CRL is …
Interaction Asymmetry: A General Principle for Learning Composable Abstractions
Learning disentangled representations of concepts and re-composing them in unseen ways
is crucial for generalizing to out-of-domain situations. However, the underlying properties of …
is crucial for generalizing to out-of-domain situations. However, the underlying properties of …
On the Identifiability of Quantized Factors
Disentanglement aims to recover meaningful latent ground-truth factors from the observed
distribution solely, and is formalized through the theory of identifiability. The identifiability of …
distribution solely, and is formalized through the theory of identifiability. The identifiability of …
Causal Representation Learning from Multimodal Biological Observations
Prevalent in biological applications (eg, human phenotype measurements), multimodal
datasets can provide valuable insights into the underlying biological mechanisms. However …
datasets can provide valuable insights into the underlying biological mechanisms. However …
Continual Learning of Nonlinear Independent Representations
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 …
representation learning as it provides deep insights into the dataset. Identifiability, as the …
Causal Temporal Representation Learning with Nonstationary Sparse Transition
Causal Temporal Representation Learning (Ctrl) methods aim to identify the temporal
causal dynamics of complex nonstationary temporal sequences. Despite the success of …
causal dynamics of complex nonstationary temporal sequences. Despite the success of …
Identification of Nonparametric Dynamic Causal Structure and Latent Process in Climate System
The study of learning causal structure with latent variables has advanced the understanding
of the world by uncovering causal relationships and latent factors, eg, Causal …
of the world by uncovering causal relationships and latent factors, eg, Causal …