The treatment-response-time graph: Interactive visualization of 'causal' relationships

Scientists have long recognized that visualization is a powerful tool for data intensive and causally oriented research. Take for instance the vivid discussions on directed-acyclic graphs (DAGs). In this study we introduce the treatment-response-time-graph (TRTG) as a way of visualizing causal relationships between treatment and response variables. We provide various rationales of why visualizing causal relationships using this approach is helpful. Among other things we argue that this framework of visualization invites us to think more systematically about the theoretical structure of a causal relationship (1) and about the measurement process we need to capture it (2). Moreover, it pushes us to think of both treatment and response as functions over time (3), it emphasizes that we have to think of both treatment and response as continuous processes (4) and it helps to meaningfully classify both treatments and responses but also causally oriented research more generally (5). We use current examples from political science to illustrate the usefulness of this visualization framework. Besides, we intend to provide R/Shiny software that allows researcher with less advanced visualization skills to produce such graphs.