Dylan Small organized this discussion in the new journal, Observational Studies. Cochran’s 1972 article is followed by comments from: Norman Breslow Thomas Cook David Cox & Nanny Wermuth Stephen Fienberg Joseph Gastwirth & Barry Graubard Andrew Gelman Ben Hansen & Adam Sales Miguel Hernan Jennifer Hill Judea Pearl Paul Rosenbaum Donald Rubin Herbert Smith Mark […]

**Causal Inference**category.

## Michael LaCour in 20 years

In case you were wondering what “Bruno” Lacour will be doing a couple decades from now . . . James Delaney pointed me to this CNN news article, “Connecticut’s strict gun law linked to large homicide drop” by Carina Storrs: The rate of gun-related murders fell sharply in the 10 years after Connecticut implemented a […]

## What to do to train to apply statistical models to political science and public policy issues

Taylor Good writes: I am a graduate of a state school with a BS in Math and a BA in Political Science, and I was wondering if you could give me some career advice. Knowing how you got to where you are now, what path would you advise someone to take to get to where […]

## My talk at MIT this Thursday

When I was a student at MIT, there was no statistics department. I took a statistics course from Stephan Morgenthaler and liked it. (I’d already taken probability and stochastic processes back at the University of Maryland; my instructor in the latter class was Prof. Grace Yang, who was super-nice. I couldn’t follow half of what […]

## What I got wrong (and right) about econometrics and unbiasedness

Yesterday I spoke at the Princeton economics department. The title of my talk was: “Unbiasedness”: You keep using that word. I do not think it means what you think it means. The talk went all right—people seemed ok with what I was saying—but I didn’t see a lot of audience involvement. It was a bit […]

## A causal-inference version of a statistics problem: If you fit a regression model with interactions, and the underlying process has an interaction, your coefficients won’t be directly interpretable

A colleague pointed me to a recent paper, “Does Regression Produce Representative Estimates of Causal Effects?” by Peter Aronow and Cyrus Samii, which begins: With an unrepresentative sample, the estimate of a causal effect may fail to characterize how effects operate in the population of interest. What is less well understood is that conventional estimation […]

## This year’s Atlantic Causal Inference Conference: 20-21 May

Dylan Small writes: The conference will take place May 20-21 (with a short course on May 19th) and the web site for the conference is here. The deadline for submitting a poster title for the poster session is this Friday. Junior researchers (graduate students, postdoctoral fellows, and assistant professors) whose poster demonstrates exceptional research will […]

## “Thinking about the possibility of spurious correlation isn’t a matter of liking—it should be pretty much automatic.”

I agree with sociologist David Weakliem when he writes the above sentence. Here’s the full paragraph: Krugman says, “you can, if you like, try to argue that this relationship is spurious, maybe not causal.” Actually, I [Weakliem] liked his original figure, since I agree with Krugman on economic policy. But thinking about the possibility of […]

## Time-release pedagogy??

Mark Palko points to this report and writes: Putting aside my concerns with the “additional years of learning” metric (and I have a lot of them), I have the feeling that there’s something strange here or i’m missing something obvious. That jump from 3-year impact to 4-year seems excessive. The press release links to a […]

## Bayesian models, causal inference, and time-varying exposures

Mollie Wood writes: I am a doctoral student in clinical and population health research. My dissertation research is on prenatal medication exposure and neurodevelopmental outcomes in children, and I’ve encountered a difficult problem that I hope you might be able to advise me on. I am working on a problem in which my main exposure […]

## The State of the Art in Causal Inference: Some Changes Since 1972

For the first issue of the journal Observational Studies, editor Dylan Small will reprint William Cochran’s 1972 article on the topic (which begins, “Observational studies are a class of statistical studies that have increased in frequency and importance during the past 20 years. In an observational study the investigator is restricted to taking selected observations […]

## Stock-and-flow and other concepts that are important in statistical modeling but typically don’t get taught to statisticians

Bill Harris writes: You’ve written about causality somewhat often, and you, along with perhaps everyone who has done anything with statistics, have written that “correlation is not causation.” When you say that correlation is not causation, you seem to be pointing out cases where correlation exists but causality does not. While that’s important, there’s another […]

## Causal Impact from Google

Bill Harris writes: Did you see http://blog.revolutionanalytics.com/2014/09/google-uses-r-to-calculate-roi-on-advertising-campaigns.html? Would that be something worth a joint post and discussion from you and Judea? I then wrote: Interesting. It seems to all depend on the choice of “control time series.” That said, it could still be a useful method. Bill replied: The good: Bayesian approaches made very approachable […]

## Six quick tips to improve your regression modeling

It’s Appendix A of ARM: A.1. Fit many models Think of a series of models, starting with the too-simple and continuing through to the hopelessly messy. Generally it’s a good idea to start simple. Or start complex if you’d like, but prepare to quickly drop things out and move to the simpler model to help […]

## “Epidemiology and Biostatistics: competitive or complementary?”

Mohammad Mansournia writes: I have a 20 minute lecture on “Epidemiology and Biostatistics: competitive or complementary?” at Tehran University of Medical Sciences in the next month. I should mention the difference between an epidemiologist and a biostatistician and their competitive or complementary roles in public health. I am wondering if you have any thoughts on […]

## Designing a study to see if “the 10x programmer” is a real thing

Lorin H. writes: One big question in the world of software engineering is: how much variation is there in productivity across programmers? (If you google for “10x programmer” you’ll see lots of hits). Let’s say I wanted to explore this research question with a simple study. Choose a set of participants at random from a […]

## If observational studies are outlawed, then only outlaws will do observational studies

My article “Experimental reasoning in social science” begins as follows: As a statistician, I was trained to think of randomized experimentation as representing the gold standard of knowledge in the social sciences, and, despite having seen occasional arguments to the contrary, I still hold that view, expressed pithily by Box, Hunter, and Hunter (1978) that […]

## Retrospective clinical trials?

Kelvin Leshabari writes: I am a young medical doctor in Africa who wondered if it is possible to have a retrospective designed randomised clinical trial and yet be sound valid in statistical sense. This is because to the best of my knowledge, the assumptions underlying RCT methodology include that data is obtained in a prospective […]

## The history of MRP highlights some differences between political science and epidemiology

Responding to a comment from Thomas Lumley (who asked why MRP estimates often seem to appear without any standard errors), I wrote: In political science, MRP always seems accompanied by uncertainty estimates. However, when lots of things are being displayed at once, it’s not always easy to show uncertainty, and in many cases I simply […]

## I love it when I can respond to a question with a single link

Shira writes: This came up from trying to help a colleague of mine at Human Rights Watch. He has several completely observed variables X, and a variable with 29% missing, Y. He wants a histogram (and other descriptive statistics) of a “filled in” Y. He can regress Y on X, and impute missing Y’s from […]