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Comparing ISPs on broadband speed is challenging, since measurements can varydue to subscriber attributes such as operation system and test conditions suchas access capacity, server distance, TCP window size, time-of-day, and networksegment size. In this paper, we draw inspiration from observational studies inmedicine, which face a similar challenge in comparing the effect of treatmentson patients with diverse characteristics, and have successfully tackled thisusing "causal inference" techniques for {\em post facto} analysis of medicalrecords. Our first contribution is to develop a tool to pre-process andvisualize the millions of data points in M-Lab at various time- andspace-granularities to get preliminary insights on factors affecting broadbandperformance. Next, we analyze 24 months of data pertaining to twelve ISPsacross three countries, and demonstrate that there is observational bias in thedata due to disparities amongst ISPs in their attribute distributions. For ourthird contribution, we apply a multi-variate matching method to identifysuitable cohorts that can be compared without bias, which reveals that ISPs arecloser in performance than thought before. Our final contribution is to refineour model by developing a method for estimating speed-tier and re-applymatching for comparison of ISP performance. Our results challenge conventionalrankings of ISPs, and pave the way towards data-driven approaches for unbiasedcomparisons of ISPs world-wide.

Comparing Broadband ISP Performance using Big Data from M-Lab