Title: Causally Constrained Data Synthesis For Private Data Release. Abstract: Data privacy is critical in many decision-making contexts, such as healthcare and finance. A common mechanism is to create differentially private synthetic data using generative models. Such data generation reflects certain statistical properties of the original data, but often has an unacceptable privacy vs. utility trade-off. Since natural data inherently exhibits causal structure, we propose incorporating \emph{causal information} into the training process to favorably navigate the aforementioned trade-off. Under certain assumptions for linear gaussian models and a broader class of models, we theoretically prove that causally informed generative models provide better differential privacy guarantees than their non-causal counterparts. We evaluate our proposal using variational autoencoders, and demonstrate that the trade-off is mitigated through better utility for comparable privacy.