Posts Tagged ‘ Causal Inference ’

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

April 4, 2015
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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 […] The post “Thinking about the possibility of spurious correlation isn’t a matter of liking—it…

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Time-release pedagogy??

March 31, 2015
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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 […] The post Time-release pedagogy?? appeared first on Statistical Modeling, Causal Inference, and Social Science.

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Bayesian models, causal inference, and time-varying exposures

March 20, 2015
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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 post Bayesian models, causal inference, and time-varying exposures appeared first on Statistical Modeling,…

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

March 16, 2015
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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 […] The post The State of the Art in Causal Inference: Some Changes Since 1972…

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

March 12, 2015
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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 […] The post Stock-and-flow and other concepts that are important in statistical modeling but typically…

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Causal Impact from Google

March 8, 2015
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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 […] The post Causal Impact from Google appeared first on Statistical Modeling, Causal Inference, and…

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Six quick tips to improve your regression modeling

January 29, 2015
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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 […] The post Six quick tips to improve your regression modeling appeared first on Statistical…

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A bunch of papers

January 20, 2015
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A bunch of papers

The beginning of the new year has been particularly busy, as I'm working on several interesting projects. On the bright side, some of these are starting to give their fruits and, coincidentally, in the last few days we've had a few papers finalise...

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“Epidemiology and Biostatistics: competitive or complementary?”

January 11, 2015
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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 […] The post “Epidemiology and Biostatistics: competitive or complementary?” appeared first on Statistical Modeling, Causal…

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

December 4, 2014
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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 […] The post Designing a study to see if “the 10x programmer” is a real…

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