Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions

TL;DR

This paper introduces a causal inference-based method for domain adaptation that predicts invariant conditional distributions across different domains without prior causal knowledge, validated on simulated and real data.

Contribution

It proposes a novel approach leveraging causal inference to address domain adaptation without requiring causal graph or intervention details.

Findings

Effective in predicting target variables across domains

Works on both simulated and real-world data

Does not depend on prior causal knowledge

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.