Can science guide policy?

TLDR: It can guide it but it cannot determine it.

In a recent MedPage Today OpEd “What Does ‘Follow the Science’ Mean, Anyway?“, Vinay Prasad argues that science alone is not sufficient to guide policy and that and that to inform decision making, it needs to be supplemented with an appropriate value system. In his words:

… science will never be sufficient to guide choices and trade-offs. Science cannot make value judgments.

If we replace “guide” with “determine”, I agree and I would like to clarify how a value judgment can be incorporated in the context of probabilistic inference. Probabilities alone are not sufficient to guide decision-making as they generally do not account for the costs and benefits of a set of possible actions. In other words, knowing the probability that it is going to rain is not enough to decide if you should carry an umbrella — you need to weigh that by the cost of the umbrella and by how much you hate getting wet. From this, you can see that it could be perfectly rational for two different people to act differently under the same weather forecast.

Decision theory, a science that is concerned with making rational decisions, has a long literature on how to encode these costs and benefits — economists call these utility functions, and statisticians, being a more pessimistic bunch, call them loss functions (U = -L). There is nothing unscientific about utility functions as we can study how closely they match people’s risk and reward preferences. So given that we can specify a utility function for say a vaccination policy, we can integrate it over our uncertainty (from the probabilistic model that includes Pr(efficacy)) and maximize this function with respect to the set of contemplated actions. This process can then guide policy by choosing the action with the highest utility. See, for example, Lin et al. (1999) which works out a policy recommendation for home radon measurement and remediation.

Of course, there is a caveat. Even assuming you can write down a set of realistic utility functions, a very difficult task in itself, who’s utility should we choose to maximize? This is where science is completely silent. It does not take a lot of imagination to realize that the utilities of any set of individuals, a utility of a corporation, and a utility of a population as a whole, are likely different. They may be similar but they are not the same. It is in that sense that science can not determine policy — the final choice of one utility function from a set of possible utilities must incorporate the most relevant value system in a society where it is to be applied. People must choose that, science can’t help you there.

References

Lin, C.-Y., Gelman, A., Price, P. N., & Krantz, D. H. (1999). Analysis of Local Decisions Using Hierarchical Modeling, Applied to Home Radon Measurement and Remediation (No. 3; pp. 305–337).

Learning Bayes from books and online classes

In 2012 I wrote a couple of posts on how to learn statistics without going to grad school. Re-reading it now, it still seems like pretty good advice, although it’s a bit too machine learning and Coursera heavy for my current tastes. One annoying gap at the time was the lack of online resources for learning Bayesian statistics. This is no longer the case, and so here are my top three resources for learning Bayes.

Richard McElreath from the Max Planck Institute for Evolutionary Anthropology recently published the second edition of Statistical Rethinking. In the book, he builds up to inference from probability and first principles and assumes only a basic background in math. I don’t love the obscure chapter names (makes it hard to figure out what’s inside) but this is the kind of book I wish I had when I was learning statistics. The example code had been ported to lots of languages including Stan, PyMC3, Julia, and more. Richard is currently teaching a class called “Statistical Rethinking: A Bayesian Course” with all the materials including lecture videos available on GitHub. For updated videos, check out his YouTube channel.

Aki Vehtari from Aalto University in Finland released his popular Bayesian Data Analysis course online — you can now take it at your own pace. This course uses the 3rd edition of the Bayesian Data Analysis book, available for free in PDF form. This is probably the most comprehensive Bayesian course on the Internet today — his demos in R and Python, lecture notes, and videos are all excellent. I highly recommend it.

For those of us who learned statistics the wrong way or who want to see the comparison to frequentist methods, see Ben Lambert’s “A Student’s Guide to Bayesian Statistics.” His corresponding YouTube lectures are excellent and I refer to them often.

Although not explicitly focused on Bayesian Inference, Regression and Other Stories by Andrew Gelman, Jenifer Hill, and Aki Vehtari is a great book on how to build up and evaluate common regression models while using Bayesian software (rstanarm package). The book covers Causal Inference, which is an unusual and welcome addition to an applied regression book. The book does not cover hierarchical models which will be covered in the not-yet-released “Applied Regression and Multilevel Models.” All the code examples are available on Aki’s website. Aki also has a list of his favorite statistics books.

Finally, I would be remiss not to mention my favorite probability book called “Introduction to Probability” by Joe Blitzstein. The book is available for free in PDF form. Joe has a corresponding class on the EdX platform and his lecture series on YouTube kept me on my spin bike for many morning hours. Another great contribution from team Joe (compiled by William Chen and Joe Blitzstein, with contributions from Sebastian Chiu, Yuan Jiang, Yuqi Hou, and Jessy Hwang) is the probability cheat sheet, currently in its second edition.

What are your favorite Bayesian resources on the Internet? Let us know in the comments.