This abstract describes a survey on deep causal estimators for a social science audience. While the machine learning community has moved quickly to leverage causal reasoning to improve predictive models, adoption of deep learning has been slower in areas of science that prioritize interpretability and robust evidence of causality for inference (e.g., epidemiology, social science, social statistics). Here we summarize deep learning models that adjust for confounding in creative ways (e.g., representation learning and generative modeling) to estimate/predict unbiased treatment effects, and/or extend causal inference beyond tabular data to text and networks. We discuss the strengths and weaknesses of these models from an applied social science perspective, and how the machine learning community might better frame/support their contributions to increase adoption by social and data scientists.