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Archive of posts filed under the Causal Inference category.

Killer O

Taggert Brooks points to this excellent news article by George Johnson, who reports: Epidemiologists have long been puzzled by a strange pattern in their data: People living at higher altitudes appear less likely to get lung cancer. . . . The higher you live, the thinner the air, so maybe oxygen is a cause of […]

One-day workshop on causal inference (NYC, Sat. 16 July)

James Savage is teaching a one-day workshop on causal inference this coming Saturday (16 July) in New York using RStanArm. Here’s a link to the details: One-day workshop on causal inference Here’s the course outline: How do prices affect sales? What is the uplift from a marketing decision? By how much will studying for an […]

About that claim that police are less likely to shoot blacks than whites

Josh Miller writes: Did you see this splashy NYT headline, “Surprising New Evidence Shows Bias in Police Use of Force but Not in Shootings”? It’s actually looks like a cool study overall, with granular data, and a ton of leg work, and rich set of results that extend beyond the attention grabbing headline that is […]

Causal and predictive inference in policy research

Todd Rogers pointed me to a paper by Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan, and Ziad Obermeyer that begins: Empirical policy research often focuses on causal inference. Since policy choices seem to depend on understanding the counterfactual—what happens with and without a policy—this tight link of causality and policy seems natural. While this link holds […]

Causal mediation

Judea Pearl points me to this discussion with Kosuke Imai at a conference on causal mediation. I continue to think that the most useful way to think about mediation is in terms of a joint or multivariate outcome, and I continue to think that if we want to understand mediation, we need to think about […]

When doing causal inference, define your treatment decision and then consider the consequences that flow from it

Danielle Fumia writes: I am a research at the Washington State Institute for Public Policy, and I work on research estimating the effect of college attendance on earnings. Many studies that examine the effect of attending college on earnings control for college degree receipt and work experience. These models seem to violate the practice you […]

Bias against women in academia

I’m not the best one to write about this: to the extent that there’s bias in favor of men, I’ve been a beneficiary. Also I’m not familiar with the research on the topic. I know there are some statistical difficulties in setting up these causal questions, comparable to the difficulties arising in using “hedonic regression” […]

The Access to Justice Lab at Harvard Law School: Job Openings!

Jim Greiner writes: The Access to Justice Lab is a startup effort, initially supported by the Laura and John Arnold Foundation with sufficient funds for three years, headed by Jim Greiner at Harvard Law School. The Lab will produce randomized control trials (“RCTs”) directly involving courts and lawyers, particularly in the areas of access to […]

The causal inference competition you’ve all been waiting for!

Jennifer Hill announces “the first-ever ACIC causal inference data analysis competition”: Is your SATT where it’s at? Participate by submitting treatment effect estimates across a range of datasets OR by submitting a function (in any of a variety of programming languages) that will take input (covariate, treatment assignment, and response) and generate a treatment effect […]

A short answer to a short question

Emir Efendic writes: What is your opinion and can you think of any critiques of the multiple mediation models by Preacher and Hayes (e.g. Preacher & Hayes, 2008)? What would be your method of choice if you were testing multiple possible mediators of an effect, but also if said mediators are connected in a model […]

2016 Atlantic Causal Inference Conference

Jennifer Hill writes: Registration for the 2016 Atlantic Causal Inference Conference is now live. Stay tuned for short course registration (free for conference participants) and an announcement regarding a causal inference data analysis competition…both coming soon! Also please consider signing up to give a lightning talk (link on website). The conference will be held 26-27 […]

Postdoc in Alabama on obesity-related research using statistics

David Allison writes:

In the biggest advance in applied mathematics since the most recent theorem that Stephen Wolfram paid for . . .

Seth Green writes: I thought you might enjoy this update from the STATA team: . . . suppose we wish to know the effect on employment status of a job training program. Further suppose that motivation affects employment status and motivation affects participation. We do not observe motivation. We have an endogeneity problem. Stata 14’s […]

Numbers too good to be true? Or: Thanks, Obama!?

This post is by Phil. The “Affordable Care Act” a.k.a. “Obamacare” was passed in 2010, with its various pieces coming into play over the following few years. One of those pieces is penalties for hospitals that see high readmission rates. The theory here, or at least one of the theories here, was that hospitals could […]

Kalesan, Fagan, and Galea respond to criticism of their paper on gun laws and deaths

The other day we posted some remarks on a recent paper by Bindu Kalesan, Jeffrey Fagan, Sandro Galea, “Firearm legislation and firearm mortality in the USA: a cross-sectional, state-level study.” In response to the criticisms from me and various commenters, the authors of the paper prepared a detailed response, which I’m linking to here. They […]

“Why this gun control study might be too good to be true”

Jeff Lax points us to this news article by Carolyn Johnson discussing a research paper, “Firearm legislation and firearm mortality in the USA: a cross-sectional, state-level study,” by Bindu Kalesan, Matthew Mobily, Olivia Keiser, Jeffrey Fagan, and Sandro Galea, that just appeared in the medical journal The Lancet. Here are the findings from Kalesan et […]

Fundamental difficulty of inference for a ratio when the denominator could be positive or negative

I happened to come across this post from 2011, which in turn is based on thoughts of mine from about 1993. It’s important and most of you probably haven’t seen it, so here it is again: Ratio estimates are common in statistics. In survey sampling, the ratio estimate is when you use y/x to estimate […]

Postdoc opportunity with Sophia Rabe-Hesketh and me in Berkeley!

Sophia writes: Mark Wilson, Zach Pardos and I are looking for a postdoc to work with us on a range of projects related to educational assessment and statistical modeling, such as Bayesian modeling in Stan (joint with Andrew Gelman). See here for more details. We will accept applications until February 26. The position is for […]

Why are trolls so bothersome?

We don’t get a lot of trolls on this blog. When people try, I typically respond with some mixture of directness and firmness, and the trolls either give up or perhaps they recognize that I am answering questions in sincerity, which does not serve their trollish purposes. But I’m pretty sure that my feeling is […]

The PACE trial and the problems with discrete, yes/no thinking

I don’t often read the Iranian Journal of Cancer Prevention, but I like this quote: I was thinking more about the PACE trial. God is in every leaf of every tree. There’s been a lot of discussion about statistical problems with the PACE papers, and also about the research team’s depressing refusal to share their […]