An old problem in multivariate statistics is that linear Gaussian models are often
unidentifiable. In factor analysis, an orthogonal rotation of the factors is unidentifiable, while …

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 …

Flexibly quantifying both irreducible aleatoric and model-dependent epistemic uncertainties
plays an important role for complex regression problems. While deep neural networks in …

We study counterfactual identifiability in causal models with bijective generation
mechanisms (BGM), a class that generalizes several widely-used causal models in the …

Whenever a clinician reflects on the efficacy of a sequence of treatment decisions for a
patient, they may try to identify critical time steps where, had they made different decisions …

Counterfactual inference aims to answer retrospective" what if" questions and thus belongs
to the most fine-grained type of inference in Pearl's causality ladder. Existing methods for …

We consider the problem of estimating the counterfactual joint distribution of multiple
quantities of interests (eg, outcomes) in a multivariate causal model extended from the …

We propose OCDaf, a novel order-based method for learning causal graphs from
observational data. We establish the identifiability of causal graphs within multivariate …

Heretofore, learning the directed acyclic graphs (DAGs) that encode the cause-effect
relationships embedded in observational data is a computationally challenging problem. A …

Causal inference on networks faces challenges posed in part by violations of standard
identification assumptions due to dependencies between treatment units. Although graph …