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There is abundant observational data in the software engineering domain,whereas running large-scale controlled experiments is often practicallyimpossible. Thus, most empirical studies can only report statisticalcorrelations -- instead of potentially more insightful and robust causalrelations. To support analyzing purely observational data for causal relations,and to assess any differences between purely predictive and causal models ofthe same data, this paper discusses some novel techniques based on structuralcausal models (such as directed acyclic graphs of causal Bayesian networks).Using these techniques, one can rigorously express, and partially validate,causal hypotheses; and then use the causal information to guide theconstruction of a statistical model that captures genuine causal relations --such that correlation does imply causation. We apply these ideas to analyzingpublic data about programmer performance in Code Jam, a large world-wide codingcontest organized by Google every year. Specifically, we look at the impact ofdifferent programming languages on a participant's performance in the contest.While the overall effect associated with programming languages is weak comparedto other variables -- regardless of whether we consider correlational or causallinks -- we found considerable differences between a purely associational and acausal analysis of the very same data. The takeaway message is that even animperfect causal analysis of observational data can help answer the salientresearch questions more precisely and more robustly than with just purelypredictive techniques -- where genuine causal effects may be confounded.

Towards Causal Analysis of Empirical Software Engineering Data: The Impact of Programming Languages on Coding Competitions