Domain adaptation under structural causal models
This work addresses domain adaptation for statistical machine learning, providing theoretical insights and a new method, but it is incremental as it builds on existing DA approaches.
The authors tackled the problem of domain adaptation (DA) by proposing a theoretical framework using structural causal models to analyze and compare DA methods, and introduced a new method called CIRM that outperforms existing methods when both covariates and label distributions are perturbed in target data.
Domain adaptation (DA) arises as an important problem in statistical machine learning when the source data used to train a model is different from the target data used to test the model. Recent advances in DA have mainly been application-driven and have largely relied on the idea of a common subspace for source and target data. To understand the empirical successes and failures of DA methods, we propose a theoretical framework via structural causal models that enables analysis and comparison of the prediction performance of DA methods. This framework also allows us to itemize the assumptions needed for the DA methods to have a low target error. Additionally, with insights from our theory, we propose a new DA method called CIRM that outperforms existing DA methods when both the covariates and label distributions are perturbed in the target data. We complement the theoretical analysis with extensive simulations to show the necessity of the devised assumptions. Reproducible synthetic and real data experiments are also provided to illustrate the strengths and weaknesses of DA methods when parts of the assumptions in our theory are violated.