Tag Archives: Statistics & Econometrics

.[embedded content] A popular idea in quantitative social sciences is to think of a cause (C) as something that increases the probability of its effect or outcome (O). That is: P(O|C) > P(O|-C) However, as is also well-known, a correlation between two variables, say A and B, does not necessarily imply that that one is a cause of the other, or the other way around, since they may both be an effect of a common cause, C. In statistics and econometrics we usually solve this...

Inverse probability weighting (student stuff) .[embedded content]

In search of identification — instrumental variables We need relevance and validity. How realistic is validity, anyway? We ideally want our instrument to behave just like randomization in an experiment. But in the real world, how likely is that to actually happen? Or, if it’s an IV that requires control variables to be valid, how confident can we be that the controls really do everything we need them to? In the long-ago times, researchers were happy to...

Does drinking cause you to become a man? Breaking news! Using advanced multiple nonlinear regression models similar to those in recent news stories on alcohol and dairy and more than 3.6M observations from 1997 through 2012, I have found that drinking more causes people to turn into men! Across people drinking 0-7 drinks per day, each drink per day causes the drinker’s probability of being a man to increase by 10.02 percentage points (z=302.2,...

Laplace’s rule of succession and Bayesian priors After their first night in paradise, and having seen the sun rise in the morning, Adam and Eve was wondering if they were to experience another sunrise or not. Given the rather restricted sample of sunrises experienced, what could they expect? According to Laplace’s rule of succession, the probability of an event E happening after it has occurred n times is p(E|n) = (n+1)/(n+2). The probabilities can be...

Beta distribution (student stuff) .[embedded content]

DAG thinking (student stuff) .[embedded content]

Maximum likelihood estimation (student stuff) .[embedded content]

P-values vs. Bayes factors (student stuff) .[embedded content]

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