Following up on this discussion [Designing an animal-like brain: black-box “deep learning algorithms” to solve problems, with an (approximately) Bayesian “consciousness” or “executive functioning organ” that attempts to make sense of all these inferences], Mike Betancourt writes: I’m not sure AI (or machine learning) + Bayesian wrapper would address the points raised in the paper. […]

**Decision Theory**category.

## Causal inference using data from a non-representative sample

Dan Gibbons writes: I have been looking at using synthetic control estimates for estimating the effects of healthcare policies, particularly because for say county-level data the nontreated comparison units one would use in say a difference-in-differences estimator or quantile DID estimator (if one didn’t want to use the mean) are not especially clear. However, given […]

## Local data, centralized data analysis, and local decision making

Jeffrey Wagar writes: Nobel-prize winning economist Oliver Williamson’s excellent book “Markets & Hierarchies” offered a set of rules for when Markets were the better method for solving a problem, or when Hierarchies [Government] was better suited. Yet could it be that some of today’s social problems persist because they are not well suited to being […]

## How to design and conduct a subgroup analysis?

Brian MacGillivray writes: I’ve just published a paper that draws on your work on the garden of forking paths, as well as your concept of statistics as being the science of defaults. The article is called, “Characterising bias in regulatory risk and decision analysis: An analysis of heuristics applied in health technology appraisal, chemicals regulation, […]

## All cause and breast cancer specific mortality, by assignment to mammography or control

Paul Alper writes: You might be interested in the robocall my wife received today from our Medicare Advantage organization (UCARE Minnesota). The robocall informed us that mammograms saved lives and was available free of charge as part of her health insurance. No mention of recent studies criticizing mammography regarding false positives, harms of biopsies, etc. […]

## “Mainstream medicine has its own share of unnecessary and unhelpful treatments”

I have a story and then a question. The story Susan Perry (link sent by Paul Alper) writes: Earlier this week, I [Perry] highlighted two articles that exposed the dubious history, medical ineffectiveness and potential health dangers of popular alternative “therapies.” Well, the same can be said of many mainstream conventional medical practices, as investigative […]

## Using statistical prediction (also called “machine learning”) to potentially save lots of resources in criminal justice

John Snow writes: Just came across this paper [Human Decisions and Machine Predictions, by Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan] and I’m wondering if you’ve been following the debate/discussion around these criminal justice risk assessment tools. I haven’t read it carefully or fully digested the details. On the surface, their […]

## Chris Moore, Guy Molyneux, Etan Green, and David Daniels on Bayesian umpires

Kevin Lewis points us to a paper by Etan Green and David Daniels, who conclude that “decisions of [baseball] umpires reflect an accurate, probabilistic, and state-specific understanding of their rational expectations—as well as an ability to integrate those prior beliefs in a manner that approximates Bayes rule.” This is similar to what was found in […]

## Don’t always give ’em what they want: Practicing scientists want certainty, but I don’t want to offer it to them!

Stephen Senn writes: What the practicing scientist wants to know is what is a good test in practice. I agree with Stephen Senn on most things—even where it seems we disagree, I think we agree on the fundamentals—but in this case I think you have to be careful about giving the practicing scientist what he […]

## The Pandora Principle in statistics — and its malign converse, the ostrich

The Pandora Principle is that once you’ve considered a possible interaction or bias or confounder, you can’t un-think it. The malign converse is when people realize this and then design their studies to avoid putting themselves in a position where they have to consider some potentially important factor. For example, suppose you’re considering some policy […]

## Wolfram on Golomb

I was checking out Stephen Wolfram’s blog and found this excellent obituary of Solomon Golomb, the mathematician who invented the maximum-length linear-feedback shift register sequence, characterized by Wolfram as “probably the single most-used mathematical algorithm idea in history.” But Golomb is probably more famous for inventing polyominoes. The whole thing’s a good read, and it […]

## Died in the Wool

Garrett M. writes: I’m an analyst at an investment management firm. I read your blog daily to improve my understanding of statistics, as it’s central to the work I do. I had two (hopefully straightforward) questions related to time series analysis that I was hoping I could get your thoughts on: First, much of the […]

## How to design future studies of systemic exercise intolerance disease (chronic fatigue syndrome)?

Someone named Ramsey writes on behalf of a self-managed support community of 100+ systemic exercise intolerance disease (SEID) patients. He read my recent article on the topic and had a question regarding the following excerpt: For conditions like S.E.I.D., then, the better approach may be to gather data from people suffering “in the wild,” combining […]

## Hey—here are some tools in R and Stan to designing more effective clinical trials! How cool is that?

In statistical work, design and data analysis are often considered separately. Sometimes we do all sorts of modeling and planning in the design stage, only to analyze data using simple comparisons. Other times, we design our studies casually, even thoughtlessly, and then try to salvage what we can using elaborate data analyses. It would be […]

## Classical statisticians as Unitarians

[cat picture] Christian Robert, Judith Rousseau, and I wrote: Several of the examples in [the book under review] represent solutions to problems that seem to us to be artificial or conventional tasks with no clear analogy to applied work. “They are artificial and are expressed in terms of a survey of 100 individuals expressing support […]

## Statisticians and economists agree: We should learn from data by “generating and revising models, hypotheses, and data analyzed in response to surprising findings.” (That’s what Bayesian data analysis is all about.)

Kevin Lewis points us to this article by economist James Heckman and statistician Burton Singer, who write: All analysts approach data with preconceptions. The data never speak for themselves. Sometimes preconceptions are encoded in precise models. Sometimes they are just intuitions that analysts seek to confirm and solidify. A central question is how to revise […]