Deep learning has become a go-to solution for many problems. This increases the importance of our ability to understand and improve these technologies. While many tools exist to support debugging deep learning models (e.g., DNNs), few attempt to provide support for understanding the root cause of unexpected behavior. Causal testing is a technique that has been shown to help developers understand and fix the root cause of defects. Causal testing may be particularly valuable in DNNs, where causality is often hard to understand due to the abstractions created for representing data. In theory, Causal testing is capable of supporting root cause debugging in various kinds of programs and software systems. However, the only implementation that exists is in Java and was not implemented as an end-to-end tool or for use on DNNs, making validation of this theory difficult. In this paper, we introduce py-holmes, a prototype tool that supports causal testing on Python programs, for both DNNs and traditional, shallow, programs. For more information about py-holmes’s internal process, see our GitHub repository: https://go.gmu.edu/pyHolmes_Public_Repo. Our demo video can be found here: https://go.gmu.edu/pyholmes_demo_2024.