Lars P. Syll

.[embedded content] Many economists — yours truly included — are highly sceptical of the ability of mainstream economics to deliver useful models. Some of us even question the ‘modern’ insistence on modelling — “if it’s not in a model, it’s not economics.” Even if we accept the limitation of only being able to say something about (some kind of) average treatment effects when using instrumental-variables designs, a significant and major problem is that researchers who use these...

Manipulability — Pearl vs Rubin (wonkish) Pearl asserts, while some RCM (Rubin Causal Models) theorists deny, that so-called “non-manipulable” variables can be causes (Pearl 2019; Holland 1986, 2008). Race and gender, which arguably cannot be experimentally manipulated, are key examples of such variables … My response is that although advocates of the frameworks adopt conflicting positions regarding certain variables, these positions are not forced upon...

Cancel Culture — Angriff auf die Pressefreiheit .[embedded content] Ein besorgniserregender Aspekt des Wokismus ist seine Besessenheit von Viktimisierung. Die Wokisten konzentrieren sich auf kollektive Viktimisierung anstelle des Individuums. Sie klassifizieren Individuen nach ihrer Zugehörigkeit zu Identitätsgruppen und weisen ihnen einen Opferstatus gemäß diesen Kriterien zu. Diese Herangehensweise reduziert Individuen auf ihre Gruppenidentität und...

Ignorability — a questionable assumption Researchers adhering to missing data analysis invariably invoke an ad-hoc assumption called “conditional ignorability,” often decorated as “ignorable treatment assignment mechanism”, which is far from being “well understood” by those who make it, let alone those who need to judge its plausibility. For readers versed in graphical modeling, “conditional ignorability” is none other than the back-door criterion that...

The dangers of too much control You see it all the time in studies. “We controlled for…” And then the list starts … The more things you can control for, the stronger your study is — or, at least, the stronger your study seems. Controls give the feeling of specificity, of precision. But sometimes, you can control for too much. Sometimes you end up controlling for the thing you’re trying to measure … An example is research around the gender wage gap, which...

In 2022, the U.S. National Institutes of Health (NIH) placed a large bet on an experimental drug developed to limit brain damage after strokes … The gamble seemed warranted. Lab studies, most by a longtime grantee, prominent University of Southern California (USC) neuroscientist Berislav Zlokovic, had generated promising data. A small safety study of the drug, sponsored by a company Zlokovic co-founded called ZZ Biotech, was also encouraging. Because of its potential to...

Busting the natural rate of unemployment myth Almost sixty years ago Milton Friedman wrote an (in)famous article arguing that (1) the natural rate of unemployment was independent of monetary policy and that (2) trying to keep the unemployment rate below the natural rate would only give rise to higher and higher inflation. The hypothesis has always been controversial, and much theoretical and empirical work has questioned the real-world relevance of the idea...

‘New Keynesianism’ — more useless than ever .Macroeconomic models may be an informative tool for research. But if practitioners of ‘New Keynesian’ macroeconomics do not investigate and make an effort to provide a justification for the credibility of the assumptions on which they erect their building, it will not fulfil its tasks. There is a gap between its aspirations and its accomplishments, and without more supportive evidence to substantiate its claims,...

Causal traps of statistics .[embedded content] Statistical reasoning certainly seems paradoxical to most people. Take for example Simpson’s paradox. From a theoretical perspective, it importantly shows that causality can never be reduced to a question of statistics or probabilities unless you are — miraculously — able to keep constant all other factors that influence the probability of the outcome studied. To understand causality we always have to relate it...

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