Causal Inference with Correlation Alignment

Estimating the effect of an intervention (i.e. causal inference) is a fundamental problem in fields like medicine, e-commerce, government (among others). An ideal approach requires experimental data, which is often hard to find or collect. Observational data on the other hand is in abundance, but contains systematic bias posing a great challenge for this task. In this paper, we introduce the use of two methods pioneered in domain adaptation: Euclidean and non-Euclidean correlation alignment in the context of causal inference. We evaluate their performance using widely used datasets and our results perform favourably against the current state of the art.