Tag Archives: Statistics & Econometrics

On statistics and causality Ironically, the need for a theory of causation began to surface at the same time that statistics came into being … This was a critical moment in the history of science. The opportunity to equip causal questions with a language of their own came very close to being realized but was squandered. In the following years, these questions were declared unscientific and went underground. Despite heroic efforts by the geneticist Sewall...

The insignificance of significance A significance test is a scientific instrument, and like any other instrument, it has a certain degree of precision. If you make the test more sensitive—by increasing the size of the studied population, for example—you enable yourself to see ever-smaller effects. That’s the power of the method, but also its danger. The truth is, the null hypothesis, if we take it literally, is probably just about always false. When you...

Why I am not a Bayesian Assume you’re a Bayesian turkey and hold a nonzero probability belief in hypothesis H that “people are nice vegetarians that do not eat turkeys and that every day I see the sun rises confirms my belief.” For every day you survive, you update your belief according to Bayes’ Rule P(H|e) = [P(e|H)P(H)]/P(e), where evidence e stands for “not being eaten” and P(e|H) = 1. Given that there do exist other hypotheses than H, P(e) is less than...

Heckman on where causality resides I make two main points that are firmly anchored in the econometric tradition. The first is that causality is a property of a model of hypotheticals. A fully articulated model of the phenomena being studied precisely defines hypothetical or counterfactual states. A definition of causality drops out of a fully articulated model as an automatic by-product. A model is a set of possible counterfactual worlds constructed under...

Econometrics — nothing but a second-best explanatory practice Consider two elections, A and B. For each of them, identify the events that cause a given percentage of voters to turn out. Once we have thus explained the turnout in election A and the turnout in election B, the explanation of the difference (if any) follows automatically, as a by-product. As a bonus, we might be able to explain whether identical turnouts in A and B are accidental, that is, due...

Threats to substantive relevance of natural experiments External validity poses a challenge for most kinds of research designs, of course. In true experiments in the social sciences, the study group is not usually a random sample from some underlying population. Often, the study group consists instead of a convenience sample, that is, a group of units that have been “drawn” through some nonrandom process from an underlying population. In other studies, one...

The two statistics books every social scientist should read Mathematical statistician David Freedman‘s Statistical Models and Causal Inference (Cambridge University Press, 2010)  and Statistical Models: Theory and Practice (Cambridge University Press, 2009) are marvellous books. They ought to be mandatory reading for every serious social scientist — including economists and econometricians — who doesn’t want to succumb to ad hoc assumptions and unsupported...

Neyman vs Fisher on causal inference .[embedded content]

The dangers of randomization idolatry How, then, can social scientists best make inferences about causal effects? One option is true experimentation … Random assignment ensures that any differences in outcomes between the groups are due either to chance error or to the causal effect … If the experiment were to be repeated over and over, the groups would not differ, on average, in the values of potential confounders. Thus, the average of the average...

Statistical significance and effect size (student stuff) .[embedded content]