“Our data science team reviewed this report and found it to be flawed.” So said an Uber spokesperson upon the release of a Stigler Center working paper, which showed that when Uber and Lyft entered a city, traffic fatalities increased by 3 percent. Overall, that equates to almost 1000 more traffic-related deaths per year.

You can see why Uber would want to dismiss this study. Indeed, the spokeswoman went on to cite its “problematic methodology” and “unjustified conclusions” but offered no analysis to support her assertions.

This is but one example of a much bigger issue that is not discussed publicly, and which has very real consequences, particularly in our data-driven world. Companies control their data. They tend to share that data with researchers who use it only in ways that are blessed by the corporations. Thus, important questions aren’t answerable, or worse, the apparent answers might be biased or incomplete.

Is there a solution?

The Uber/Lyft imbroglio first came to my attention a year ago, when I reviewed the Stigler Center working paper. The study, by John Barrios and co-authors, was only correlational, because, without access to Uber or Lyft’s data, Barrios et al. could not determine whether this increase in fatalities was simply the result of increased traffic or an inferior quality of ride-hailing drivers. Furthermore, they could not (and did not) even claim that the observed correlation was necessarily the result of a causal link between ride-hailing and accidents. To prove causality, one would need an improbable experiment where cities are randomly assigned to a treatment group (the entry of ride-hailing) and a control group. Yet, the authors did their best to dispel possible alternative explanations. In sum, the study raised an important question: What are the externalities produced by ride-hailing?