In this paper, we present ENTER, an interpretable Video Question Answering(VideoQA) system based on event graphs. Event graphs convert videos intographical representations, where video events form the nodes and event-eventrelationships (temporal/causal/hierarchical) form the edges. This structuredrepresentation offers many benefits: 1) Interpretable VideoQA via generatedcode that parses event-graph; 2) Incorporation of contextual visual informationin the reasoning process (code generation) via event graphs; 3) Robust VideoQAvia Hierarchical Iterative Update of the event graphs. Existing interpretableVideoQA systems are often top-down, disregarding low-level visual informationin the reasoning plan generation, and are brittle. While bottom-up approachesproduce responses from visual data, they lack interpretability. Experimentalresults on NExT-QA, IntentQA, and EgoSchema demonstrate that not only does ourmethod outperform existing top-down approaches while obtaining competitiveperformance against bottom-up approaches, but more importantly, offers superiorinterpretability and explainability in the reasoning process.