Descriptive and Prescriptive Questions In Practice#
Below are two vignettes of groups struggling to answer important questions about the world. Let’s read them to see how Descriptive and Prescriptive Questions interact in the context of real world problems, and how you (as a data scientist) can wield your skills to be especially impactful.
Opioid Reductions#
You have been hired by a medical regulatory board concerned about the rise in opioid overdoses. They are debating whether they should (there’s that magic word!) make it harder for patients to get opioids. Fundamentally, however, they worry that while restrictions on opioids may reduce overdoses and addiction, they may also prevent some patients with very real pain conditions from getting the care they need.
Why are they stuck?
They may be unsure how to value overdose prevention relative to ensuring appropriate patient access to opioids, and/or
they may also be unsure about how much opioid regulations that reduce overdoses by a certain amount would limit access for patients in need.
The first of these sources of uncertainty is about a Prescriptive Question — if you could prevent one overdose death at the expense of preventing 10 patients in pain from getting the opioids they need, would you accept that trade-off? You may have an opinion on that question, but unless you have experience with overdoses, addiction, or chronic pain, you probably don’t have much to offer to a room full of doctors who have extensive experience with how these things impact patients and their families.
But the second source of uncertainty could be resolved by answering a Descriptive Question — what is the effect of opioid regulations on appropriate patient opioid access? That’s a question which you — the data scientist — is uniquely positioned to answer! You could study policies that have been implemented in the past and come up with a rigorous estimate of how much opioid regulations that reduce overdoses also reduce access for patients in need. You could also evaluate different kinds of policies to figure out which is most efficient — maybe some policies (like not allowing any opioid prescriptions at all) are good at stopping overdose deaths but also really limit appropriate access, while other policies are similarly good at reducing overdoses but have a much smaller effect on limiting access.
The Example of Carbon Emissions#
A profoundly difficult Prescriptive Question in debates over carbon reduction is whether developing countries should be held to the same emission reduction targets as more developed countries. On the one hand, developing countries like China and India are the source of most current growth in carbon emissions, and so policies that do not apply to developing countries are unlikely to prevent many of the worst climate change outcomes. On the other hand, these countries produce radically less carbon per capita than Europe or the United States, and the industrial growth creating those emissions has been a major factor in lifting billions of people out of extreme poverty.
Hard choices indeed! How does one weigh the improvements in the quality of life of those in extreme poverty against the possible consequences of even greater climate catastrophes?
While that question is, in part, a Prescriptive Question that no regression can answer, data scientists can bring data to bear on this question indirectly by helping everyone understand the potential consequences of different carbon targets for developing countries and the feasibility of different strategies for carbon reduction. A data scientist could, for example:
Evaluate the effectiveness of different messages politicians in the US and Europe could use to convince their constituents to support greater carbon reduction targets,
Quantify the magnitude of the effect on global warming caused by different emissions targets for developing countries to help politicians in developing countries weigh the poverty-reducing benefits of carbon-intensive industrialization against the likely direct effect of flooding, droughts, or more severe storms on their own citizens, or
Estimate the cost-effectiveness of developed countries sharing lower emissions industrial technologies with developing countries to ameliorate the tradeoff between poverty reduction and emissions.
In each of these cases, the data scientist is only answering Descriptive Questions, but in doing so they are helping everyone better understand the consequences of their decisions, and in doing so (hopefully) help the world to make more informed decisions about the trade-offs they are making.
Recap#
Answering Descriptive Questions — questions about how the world is or would be in different scenarios — is the core competency of the data scientist. In the chapters that follow, we will explore in detail three different kinds of Descriptive Questions: Exploratory, Passive-Prediction, and Causal Questions.
While these are the only types of questions that data science tools can answer directly, it is important for you, the data scientist, to also recognize when you encounter Prescriptive Questions — that is, questions about how the world should be, or what we ought to do. These questions can only be answered with respect to a system of values, and as such, do not have right or wrong answers, and cannot be answered by statistical means. Nevertheless, as a data scientist, you are well-prepared to help others (and yourself!) make more informed choices when they decide how to answer Prescriptive Questions for themselves.