Understanding and inferring causal relationships between variables is a fundamental task in visualization and visual analysis. However, it can be challenging to verify inferences of causal relationships from traditional observational data because they often lack a ground truth causal model, complicating the evaluation of visual causal inference tools. To address this challenge, we introduce CausalSynth, an interactive web application designed to generate synthetic datasets from user-defined causal relationships. CausalSynth enables users to define acyclic causal graphs via a user-friendly graphical interface, establish interrelationships between variables, and produce datasets that reflect these desired causal interactions. The application also includes built-in tools for visualizing the generated datasets, facilitating deeper insights into the user-defined causal structure and aiding the validation of the generated data. By providing a user-friendly interface for synthetic data generation and visualization based on ground truth causal models, CausalSynth helps support more meaningful evaluations of visual causal inference technologies.