Recently in the field of unsupervised representation learning, strong identifiability results for
disentanglement of causally-related latent variables have been established by exploiting …

It is commonplace to encounter heterogeneous or nonstationary data, of which the
underlying generating process changes across domains or over time. Such a distribution …

Abstract Machine learning approaches commonly rely on the assumption of independent
and identically distributed (iid) data. In reality, however, this assumption is almost always …

A fundamental problem in many sciences is the learning of causal structure underlying a
system, typically through observation and experimentation. Commonly, one even collects …

In many scientific fields, such as economics and neuroscience, we are often faced with
nonstationary time series, and concerned with both finding causal relations and forecasting …

Causal structure learning can reveal the causal mechanism behind natural systems. It is well
studied that the multiple domain data consisting of observational and interventional samples …

Under stringent model type and variable distribution assumptions, differentiable score-
based causal discovery methods learn a directed acyclic graph (DAG) from observational …

We investigate the relationship between system identification and intervention design in
dynamical systems. While previous research demonstrated how identifiable representation …

In many scientific applications, we observe a system in different conditions in which its
components may change, rather than in isolation. In our work, we are interested in …

A common assumption in causal inference from observational data is that there is no hidden
confounding. Yet it is, in general, impossible to verify the presence of hidden confounding …