Title: Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions,
Abstract: An important goal common to domain adaptation and causal inference is to make
accurate predictions when the distributions for the source (or training)
domain(s) and target (or test) domain(s) differ. In many cases, these different
distributions can be modeled as different contexts of a single underlying
system, in which each distribution corresponds to a different perturbation of
the system, or in causal terms, an intervention. We focus on a class of such
causal domain adaptation problems, where data for one or more source domains
are given, and the task is to predict the distribution of a certain target
variable from measurements of other variables in one or more target domains. We
propose an approach for solving these problems that exploits causal inference
and does not rely on prior knowledge of the causal graph, the type of
interventions or the intervention targets. We demonstrate our approach by
evaluating a possible implementation on simulated and real world data.