Tag Archives: Statistics & Econometrics

Are RCTs — really — the best way to establish causality? The best method is always the one that yields the most convincing and relevant answers in the context at hand. We all have our preferred methods that we think are underused. My own personal favorites are cross-tabulations and graphs that stay close to the data; the hard work lies in deciding what to put into them and how to process the data to learn something that we did not know before, or that...

Statistical assumptions and racial bias Our analysis indicates that existing empirical work in this area is producing a misleading portrait of evidence as to the severity of racial bias in police behavior. Replicating and extending the study of police behavior in New York in Fryer (2019), we show that the consequences of ignoring the selective process that generates police data are severe, leading analysts to dramatically underestimate or conceal entirely...

The poverty of fictional storytelling in statistics and econometrics  The most expedient population and data generation model to adopt is one in which the population is regarded as a realization of an infinite super population. This setup is the standard perspective in mathematical statistics, in which random variables are assumed to exist with fixed moments for an uncountable and unspecified universe of events … This perspective is tantamount to assuming a...

Decision making — trustworthiness vs relevance The random assignment plus masking are supposed to make it likely that the two groups have the same distribution of causal factors. It is controversial how confident these measures should make us that they do this. This issue bears on the trustworthiness of causal claims backed by RCTs. As we noted, trustworthiness is the central topic of many other guides. But we aim to move beyond that; we concentrate on...

‘Ideally controlled experiments’ tell us with certainty what causes what effects — but only given the right closures. Making appropriate extrapolations from (ideal, accidental, natural or quasi) experiments to different settings, populations or target systems, is not easy. ‘It works there’ is no evidence for ‘it will work here.’ Causes deduced in an experimental setting still have to show that they come with a transportability warrant to the target population. The causal...

Modularity — a questionable assumption Modularity is the mark of a type of independence from context. The same functional relationship between variables will hold in a given component of the contributing mechanisms whether or not there is a change in a different component. The total effect may change when different components contribute, but the operation of the modular mechanism will not be changed nor change them. In situations where the presence or...

In defense of DAGs .[embedded content]

Bayesian analysis with SPSS .[embedded content] Yours truly has for several years been conducting a doctoral course in statistics for students in educational science where SPSS has been used. Unfortunately, Bayesian analysis has not been available in that program. With version 29 of SPSS, things have changed.  So, next year, there will be a new addition to the course!

.[embedded content] As always a pleasure listening to Edward Leamer and his critical views on the (mis)uses of statistical methods in empirical research. Main message: without a deep understanding of context, statistical and econometric analyses are useless!

Why we need causality in science Many journal editors request authors to avoid causal language, and many observational researchers, trained in a scientific environment that frowns upon causality claims, spontaneously refrain from mentioning the C-word (“causal”) in their work … The proscription against the C-word is harmful to science because causal inference is a core task of science, regardless of whether the study is randomized or nonrandomized. Without...