Interactive Apps on Causality
We develop several applications using R shiny to illustrate important phenomena in causal inference. We are continuously adding new apps. In case you have an idea for a new app, feel free to add it to the list of causal case studies by opening a new issue.
Collider Bias: The Movie Star Example
Abstract
This app illustrates the Collider Bias in the context of the Movie Star Example from Cunningham (2021, Section 3.6.1). A CNN blogpost reported that Megan Fox was voted the worst but at the same time the most attractive star in 2009. This suggests the question: Are more attractive actors generally less talented?
Links
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The collider bias app is available via https://colliderbias.herokuapp.com/.
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The source code is available in the GitHub repository https://github.com/DigitalCausalityLab/colliderbias
Simpson’s Paradox: The Cholesterol Example
Abstract
This app illustrates the Simpson’s Paradox based on the Cholesterol Example in Glymour et al. (2016, Section 1.2). In this example, we want to assess the effect of exercise on cholesterol in various age groups. The scatter plot below suggests that overall more exercise leads to higher cholesterol. However, if we consider each of the age groups separately, the sign of the effect is negative.
Links
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The Simpson’s paradox app is available via https://simpsons-paradox.herokuapp.com/.
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The source code is available in the GitHub repository https://github.com/DigitalCausalityLab/simpsonsparadox
References
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Cunningham, Scott. “Causal inference.” Causal Inference. Yale University Press, 202, available online
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Glymour, Madelyn, Judea Pearl, and Nicholas P. Jewell. Causal inference in statistics: A primer. John Wiley & Sons, 2016.