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Randomized experiments (A/B testings) have become the standard way forweb-facing companies to guide innovation, evaluate new products, and prioritizeideas. There are times, however, when running an experiment is too complicated(e.g., we have not built the infrastructure), costly (e.g., the interventionwill have a substantial negative impact on revenue), and time-consuming (e.g.,the effect may take months to materialize). Even if we can run an experiment,knowing the magnitude of the impact will significantly accelerate the productdevelopment life cycle by helping us prioritize tests and determine theappropriate traffic allocation for different treatment groups. In this setting,we should leverage observational data to quickly and cost-efficiently obtain areliable estimate of the causal effect. Although causal inference fromobservational data has a long history, its adoption by data scientist intechnology companies has been slow. In this paper, we rectify this by providinga brief introduction to the vast field of causal inference with a specificfocus on the tools and techniques that data scientist can directly leverage. Weillustrate how to apply some of these methodologies to measure the effect ofcontributions (e.g., post, comment, like or send private messages) onengagement metrics. Evaluating the impact of contributions on engagementthrough an A/B test requires encouragement design and the development ofnon-standard experimentation infrastructure, which can consume a tremendousamount of time and financial resources. We present multiple efficientstrategies that exploit historical data to accurately estimate thecontemporaneous (or instantaneous) causal effect of a user's contribution onher own and her neighbors' (i.e., the users she is connected to) subsequentvisitation frequency. We apply these tools to LinkedIn data for several millionmembers.

Causal inference from observational data: Estimating the effect of contributions on visitation frequency atLinkedIn