Tag Archives: Statistics & Econometrics

Dynamic and static interpretations of regression coefficients When econometric and statistical textbooks present simple (and multiple) regression analysis for cross-sectional data, they often do it with regressions like “regress test score (y) on study hours (x)” and get the result y = constant + slope coefficient*x + error term. When speaking of increases or decreases in x in these interpretations, we have to remember that it is a question of...

While raising worthwhile points, most discussions I see misunderstand randomization in both causal and statistical ways. Notably, randomization can be valuable but does not induce balance in the ordinary English sense of the word, nor does it deal with most problems of real experiments. Furthermore, the use of the word “balance” to describe what randomization actually does invites confusion with the ordinary English meaning of “balance” (as does use of ordinary words like...

Do RCTs really control for ‘lack of balance’? Mike Clarke, the Director of the Cochrane Centre in the UK, for example, states on the Centre’s Web site: ‘In a randomized trial, the only difference between the two groups being compared is that of most interest: the intervention under investigation’. This seems clearly to constitute a categorical assertion that by randomizing, all other factors — both known and unknown — are equalized between the experimental...

How to compute causal effects using regression (student stuff) .[embedded content]

A beginner’s guide to Bayesian statistics (student stuff) .[embedded content]

Debating the value of randomization In Social Science and Medicine (December 2017), Angus Deaton & Nancy Cartwright argue that RCTs do not have any warranted special status. They are, simply, far from being the ‘gold standard’ they are usually portrayed as: Randomized Controlled Trials (RCTs) are increasingly popular in the social sciences, not only in medicine. We argue that the lay public, and sometimes researchers, put too much trust in RCTs over...

LATE calculation (student stuff) .[embedded content] As Scott Cunningham has it in Causal Inference: The Mixtape: How do you know when you have a good instrument? … A necessary but not sufficient condition for having an instrument that can satisfy the exclusion restriction is if people are confused when you tell them about the instrument’s relationship to the outcome.

.[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...