Comprehensively charting the biologically causal circuits that govern the phenotypic space
of human cells has often been viewed as an insurmountable challenge. However, in the last …

We study causal representation learning, the task of inferring latent causal variables and
their causal relations from high-dimensional functions (“mixtures”) of the variables. Prior …

We study the problem of learning causal representations from unknown, latent interventions
in a general setting, where the latent distribution is Gaussian but the mixing function is …

We establish conditions under which latent causal graphs are nonparametrically identifiable
and can be reconstructed from unknown interventions in the latent space. Our primary focus …

Causal representation learning has emerged as the center of action in causal machine
learning research. In particular, multi-domain datasets present a natural opportunity for …

We present a unified framework for studying the identifiability of representations learned
from simultaneously observed views, such as different data modalities. We allow a partially …

Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data
suffers notoriously from false positives. Approaches to control the false discovery rate (FDR) …

The premise of identifiable and causal representation learning is to improve the current
representation learning paradigm in terms of generalizability or robustness. Despite recent …

This paper focuses on causal representation learning (CRL) under a general nonparametric
latent causal model and a general transformation model that maps the latent data to the …

In many problems, the measured variables (eg, image pixels) are just mathematical
functions of the hidden causal variables (eg, the underlying concepts or objects). For the …