Estimating the effect of an intervention while accounting for confounding variables is a key task in causal inference. Oftentimes, the confounders are unobserved, but we have access to large amounts of unstructured data (images, text) that contain valuable proxy signal about the missing confounders. This paper demonstrates that leveraging unstructured data that is typically left unused by existing algorithms improves the accuracy of causal effect estimation. Specifically, we introduce deep multi…