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A wide variety of online platforms use digital badges to encourage users totake certain types of desirable actions. However, despite their growingpopularity, their causal effect on users' behavior is not well understood. Thisis partly due to the lack of counterfactual data and the myriad of complexfactors that influence users' behavior over time. As a consequence, theirdesign and deployment lacks general principles.  In this paper, we focus on first-time badges, which are awarded after a usertakes a particular type of action for the first time, and study their causaleffect by harnessing the delayed introduction of several badges in a popularQ&A website. In doing so, we introduce a novel causal inference framework forbadges whose main technical innovations are a robust survival-based hypothesistesting procedure, which controls for the utility heterogeneity across users,and a bootstrap difference-in-differences method, which controls for the randomfluctuations in users' behavior over time. We find that first-time badges steerusers' behavior if the utility a user obtains from taking the correspondingaction is sufficiently low, otherwise, the badge does not have a significanteffect. Moreover, for badges that successfully steered user behavior, weperform a counterfactual analysis and show that they significantly improved thefunctioning of the site at a community level.

Harnessing Natural Experiments to Quantify the Causal Effect of Badges