Visualizing Causal Scenarios and Planned | Realized Measurements [Interactively]

32 Pages Posted: 1 Apr 2017 Last revised: 30 Jan 2018

See all articles by Paul C. Bauer

Paul C. Bauer

Mannheim Centre for European Social Research (MZES)

Date Written: January 30, 2018


This study explores ways of visualizing causal scenarios, planned and realized measurements. A causal scenario describes how a treatment variable D affects a response variable Y, when and for which units. It involves the trajectories of D and Y across time, across units or groups thereof. The visual framework developed in this study emphasizes the role of time in causal relationships. We illustrate various insights it may produce and test potential applications relying on a combination of hypothetical examples and examples from the causal inference literature (e.g. Lalonde 1986, Card and Krueger 1994). As a proof-of-concept the study is supplemented with an online application (based on open-source software R, Shiny and Plotly). In the future this application should allow users to visualize examples as well as 'draw' their own scenarios interactively. Most of the graphs in this paper were made using the app and you can find a development version online.

Keywords: Data Visualization, Causal Inference, Graphical Causal Models

Suggested Citation

Bauer, Paul C., Visualizing Causal Scenarios and Planned | Realized Measurements [Interactively] (January 30, 2018). Available at SSRN: or

Paul C. Bauer (Contact Author)

Mannheim Centre for European Social Research (MZES) ( email )


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