Progress in the past decade

It’s been a busy decade for our research.

Before going on, I’d like to thank hundreds of collaborators, including students; funders from government, nonprofits, and private industry; blog commenters and people who have pointed us to inspiring research, outrages, beautiful and ugly graphs, cat pictures, and all the rest; all those of you who have shared your disagreements; pointing out my errors and my failures in communication; and, most of all, family and friends for your love and support.

Bayes and Stan

Our biggest contribution was Stan, which represents a major research effort in itself (thanks, Bob, Matt, Daniel, and so many others!), has motivated lots of research in Bayesian inference and computation, has facilitated tons of applied work by ourselves and others, and has inspired other probabilistic programming languages targeted to particular classes of models and applications.

Relatedly, during the past decade we completed the third edition of BDA (thanks, Aki!) and Regression and Other Stories (thanks, Jennifer and Aki!). Here’s a list of our published papers on Bayesian methods and computation in the past decade, in reverse chronological order:

That method developed in that paper with Pasarica did not directly came to much. But then a few years later the idea of maximizing expected squared jumped distance helped motivate Matt Hoffman to develop the very useful Nuts algorithm. This demonstrates the potential benefit of pushing through our research ideas, even when they don’t lead to anything right away.

Voting, public opinion, and sample surveys

The motivation for all the above work on Bayesian methods and computing was to make progress in applied problems. Here’s our recent published work in voting, public opinion, and sample surveys:

Wow! I’d forgotten about a lot of that.

Other applied work

And here’s our recent published work in other applied areas:

I included the zombies paper in the above list, but I really could’ve counted it as survey methods.

Open science and ethics

Recently we’ve been thinking a lot about open science and ethics:

There are a ton of papers in that list, in part in response to recent concerns about scientific replication, and in part because at the beginning of the decade I had the idea of running a regular column on ethics and statistics for Chance magazine, with the idea of putting the columns into a book. I doubt I’ll write a book specifically on ethics and statistics—I just don’t think there would be that much of an audience for it—but I’ve learned a lot from thinking about these issues.

My favorite of my articles on open science is What has happened down here is the winds have changed, from 2016; it’s not on the above list because I forgot to ever send it to a magazine or journal to be officially published, so it exists only as a blog entry.

Understanding the statistical properties of statistical methods as they are used

Related to work in open science is our research into the statistical properties of the statistical methods that people actually use. Theoretical statistics is the theory of applied statistics, so this all might be labeled real-world frequentist statistics:

History, philosophy, and statistics education

My collaborators and I have also written some things on history, philosophy, and statistics education. Much of this represents ideas that my colleagues and I have been discussing for decades, that we finally got an opportunity to write up and discuss formally. Others were responses to new ideas or developments:

Also, Deb Nolan and I came out with the second edition of Teaching Statistics: A Bag of Tricks.

Causal inference

We’ve also done some research on causal inference:

Not a lot of papers on the topic, as I’m not always clear on what I can add to these discussions—there’s a reason that Jennifer is the main author of the causal chapters in our books—but causal inference is central to statistics (recall the title of this blog!), so I’m glad to contribute to it in some way.

Statistical graphics and visualization

Visualization is one of my favorite topics that we keep coming back to, as part of our larger effort to incorporate statistical practice into formal statistical theory and methods:

That’s all only part of the story

The above list is incomplete, in that it does not include unpublished papers, blogging (we’ve had something like 6000 posts and 100,000 comments in the past decade), case studies, wiki pages, and other modes of research communication.

Let me emphasize that all this work is collaborative. Even the articles published only under my name are collaborative in that they are the results of lots of reading and discussions with others. Let’s remember to avoid the scientist-as-hero narrative.

It’s been an eventful decade in the world: economic development, environmental challenges, social and political opportunities, and nearly a billion new people. Statistical modeling, causal inference, and social science is only a tiny part of all of this, and the work of my collaborators and myself is only a tiny part of statistical modeling, causal inference, and social science—but we still try in some way to develop tools for people to be able to understand and improve our physical and social environments. My colleagues and I have been privileged to have a working environment that has allowed us to make efforts in these directions, and we’ve also worked hard—with books, research articles, journalism, blogging, software, documentation, and online forums—to engage with and build communities of people who can do similar work.