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The interconnection of devices into networked services and applications is collectively called the Internet of Things (IoT). Significant efficiencies are gained from this ecosystem that automates many aspects of everyday life. Unfortunately, this automation often occludes the understanding of causes and effects of actions taken in an IoT network (including those of rogue devices). As we observe the effects of a particular device’s actions on its environment (eg, the light turns on), we want to capture the full causality path to the originating device. This paper describes a promising approach that enables one to infer a potential sequence of actions (or any deviation thereof) that led to a particular observation.