Tag Archives: Statistics & Econometrics

Statistics and econometrics — science building on fantasy worlds In econometrics one often gets the feeling that many of its practitioners think of it as a kind of automatic inferential machine: input data and out comes casual knowledge. This is like pulling a rabbit from a hat. Great — but first you have to put the rabbit in the hat. And this is where assumptions come into the picture. The assumption of imaginary ‘super populations’ is one of the many...

Why technical fixes will not rescue econometrics On the issue of the various shortcomings of regression analysis and econometrics, no one sums it up better than David Freedman in his Statistical Models and Causal Inference: In my view, regression models are not a particularly good way of doing empirical work in the social sciences today, because the technique depends on knowledge that we do not have. Investigators who use the technique are not paying...

Science before statistics — causal inference .[embedded content]

Probability and rationality — trickier than most people think The Coin-tossing Problem My friend Ben says that on the first day he got the following sequence of Heads and Tails when tossing a coin: H H H H H H H H H H And on the second day he says that he got the following sequence: H T T H H T T H T H Which report makes you suspicious? Most people yours truly asks this question says the first report looks suspicious. But actually both reports are equally...

Reverse causal reasoning and inference to the best explanation One of the few statisticians that yours truly has on his blogroll is Andrew Gelman. Although not sharing his Bayesian leanings, I find  his thought-provoking and non-dogmatic statistical thinking highly recommendable. The plaidoyer infra for “reverse causal questioning” is typical Gelmanian: When statistical and econometrc methodologists write about causal inference, they generally focus on...

Instrumental variables — in search for identification 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...

Causal discovery and the faithfulness assumption (student stuff) .[embedded content] For more on the (questionable) faithfulness assumption, cf. chapter six of Nancy Cartwright’s Hunting causes and using them.

Conditional probabilities (student stuff) .[embedded content]

Which causal inference books to read Source All suggestions are highly readable, but for the general reader, yours truly would also like to recommend The book of why by Pearl & Mackenzie.

RCT — a questionable claim of establishing causality The ideal RCT is the special case in which the trial’s treatment status is also assigned randomly (in addition to drawing random samples from the two populations, one treated and one not) and the only error is due to sampling variability … In this special case, as the number of trials increases, the mean of the trial estimates tends to get closer to the true mean impact. This is the sense in which an...