Hopefully this will be of some interest to a few readers. More details and example code below.
Hopefully this will be of some interest to a few readers. More details and example code below.
``Unlike the Neyman–Rubin model, these [selection] models do not start with the experiment as an ideal but they start with well-posed, clearly articulated models for outcomes and treatment choice where the unobservables that underlie the selection and evaluation problem are made explicit. The hypothetical manipulations define the causal parameters of the model. Randomization is a metaphor and not an ideal or “gold standard".’’ (page 37)
Heckman, J (2008) ``Econometric Causality’’ NBER working paper #13934. http://papers.nber.org/papers/W13934
Abstract: This paper presents the econometric approach to causal modeling. It is motivated by policy problems. New causal parameters are defined and identified to address specific policy problems. Economists embrace a scientific approach to causality and model the preferences and choices of agents to infer subjective (agent) evaluations as well as objective outcomes. Anticipated and realized subjective and objective outcomes are distinguished. Models for simultaneous causality are developed. The paper contrasts the Neyman-Rubin model of causality with the econometric approach.
]]>I can't really pass judgment on the study itself -- I haven't had time to read the thing -- but as someone who is pretty obsessed with (and professionally involved in) criticizing causal inferences drawn from observational studies, I found it pretty entertaining to read the comments. I admit I did not read all 409 of them. But on the whole they fell into five categories:
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The Applied Statistics workshop meets in room N-354, CGIS-Knafel 1737 Cambridge. The workshop begins at 12noon with a light lunch, with our presentations beginning at 1215 and usually ending around 130 pm.
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Also check out the mediation item at Wikipedia and its great references.
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a) 100 students take a class, and 50 pass.
b) Given that next time, 50 students pass the (identical) class, how many students, on average, were enrolled?
The "fallacy" is in assuming that the expected number of original enrollees is 100, when it must necessarily be greater than 100 due to the uncertainty in the estimation of passing the class. The article points out that it's ignorance of the prior distribution of passing students that's at fault for the "fallacy" - I argue that it's the prior distribution of one student passing a test that's the cause of the paradox.
]]>There has been no attention to circular (purely cyclical) data in political science research. We show that such data exists and is generally mishandled by models that do not take into account the inherently recycling nature of some phenomenon. Clock and calendar effects are the obvious cases, but directional data exists as well. We develop a modeling framework based on the von Mises distribution and apply it to two datasets: casualties in the second Iraq war and suicides in Switzerland. Results clearly demonstrate the importance of circular regression models to handle periodic data.
A preliminary draft of their paper is available here
The authors also provided an example of circular data analyzed in their paper: the figure below shows the time at which different kinds of violent attacks occur in Iraq.
The applied statistics workshop meets at 12 noon in room N-354 of CGIS-Knafel (1737 Cambridge St), with a light lunch served. The presentations begin around 1215 and conclude at about 130 pm.
Please contact me with any questions
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