Consider the problem of improving the estimation of conditional average treatment effects
(CATE) for a target domain of interest by leveraging related information from a source …

In this work, we deepen on the use of normalizing flows for causal reasoning. Specifically,
we first leverage recent results on non-linear ICA to show that causal models are identifiable …

Existing machine learning methods for causal inference usually estimate quantities
expressed via the mean of potential outcomes (eg, average treatment effect). However, such …

Injecting structure into neural networks enables learning functions that satisfy invariances
with respect to subsets of inputs. For instance, when learning generative models using …

Estimating heterogeneous treatment effects is an important problem across many domains.
In order to accurately estimate such treatment effects, one typically relies on data from …

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 …

Understanding and quantifying cause and effect relationships is an important problem in
many domains. The generally-agreed standard solution to this problem is to perform a …

Normalizing Flows are a powerful type of generative model that transforms an intractable
distribution of data into a more desirable one through the use of bijective functions. Their …

The ability to answer causal questions is crucial in many domains, as causal inference
allows one to understand the impact of interventions. In many applications, only a single …

Abstract Treatment effect estimates are often available from randomized controlled trials as a
single average treatment effect for a certain patient population. Estimates of the conditional …