Biases in Data-Driven Networking, and What to Do About Them

Recent efforts highlight the promise of data-driven approaches to optimize network decisions. Many such efforts use trace-driven evaluation; i.e., running offline analysis on network traces to estimate the potential benefits of different policies before running them in practice. Unfortunately, such frameworks can have fundamental pitfalls (e.g., skews due to previous policies that were used in the data collection phase and insufficient data for specific subpopulations) that could lead to misleading estimates and ultimately suboptimal decisions. In this paper, we shed light on such pitfalls and identify a promising roadmap to address these pitfalls by leveraging parallels in causal inference, namely the Doubly Robust estimator.