Symmetry as Intervention; Causal Estimation with Data Augmentation

UZAIR AKBAR, Niki Kilbertus, Hao Shen, Krikamol Muandet, Bo Dai

Keywords: Causal Inference, Intervention, IV Regression, Invariance, Data Augmentation

TL;DR: We show the effectiveness of data-augmentation for reducing bias due to unobserved confounding, and this motivates the proposal of our novel method for the same.

Abstract: To our knowledge, we provide the first analysis of causal estimation under hidden confounding using only observational $(X, Y)$ data and knowledge of symmetries in data generation via data augmentation (DA) transformations. We show that such DA is equivalent to interventions on the treatment $X$, mitigating bias from hidden confounding, and that framing DA as a relaxation of instrumental variables (IVs)-sources of $X$ randomization that are conditionally independent of the outcome $Y$-can further improve causal estimation beyond simple DA.

Submission Number: 167