Statistics & Econometrics

All science entails human judgment, and using statistical models doesn’t relieve us of that necessity. Working with misspecified models, the scientific value of statistics is actually zero — even though you’re making valid statistical inferences! Statistical models are no substitutes for doing real science. Or as a famous German philosopher famously wrote 150 years ago: […]

. Great presentation, but I think Angrist should have also mentioned that although ‘ideally controlled experiments’ may tell us with certainty what causes what effects, this is so only when given the right ‘closures.’ Making appropriate extrapolations from — ideal, accidental, natural or quasi — experiments to different settings, populations or target systems, is not […]

There’s nothing magical about Bayes’ theorem. It boils down to the truism that your belief is only as valid as its evidence. If you have good evidence, Bayes’ theorem can yield good results. If your evidence is flimsy, Bayes’ theorem won’t be of much use. Garbage in, garbage out. The potential for Bayes abuse begins […]

For many questions in the social sciences, a research design guaranteeing the validity of causal inferences is difficult to obtain. When this is the case, researchers can attempt to defend hypothesized causal relationships by seeking data that subjects their theory to repeated falsification. Karl Popper famously argued that the degree to which we have confidence […]

An ongoing concern is that excessive focus on formal modeling and statistics can lead to neglect of practical issues and to overconfidence in formal results … Analysis interpretation depends on contextual judgments about how reality is to be mapped onto the model, and how the formal analysis results are to be mapped back into reality. […]

In Social Science and Medicine (December 2017), Angus Deaton & Nancy Cartwright argue that Randomized Controlled Trials (RCTs) do not have any warranted special status. They are, simply, far from being the ‘gold standard’ they are usually portrayed as: Contrary to frequent claims in the applied literature, randomization does not equalize everything other than the […]

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 […]

. I had learned to do integrals by various methods shown in a book that my high school physics teacher Mr. Bader had given me. [It] showed how to differentiate parameters under the integral sign – it’s a certain operation. It turns out that’s not taught very much in the universities; they don’t emphasize it. […]

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 […]

Because statistical analyses need a causal skeleton to connect to the world, causality is not extra-statistical but instead is a logical antecedent of real-world inferences. Claims of random or “ignorable” or “unbiased” sampling or allocation are justified by causal actions to block (“control”) unwanted causal effects on the sample patterns. Without such actions of causal […]