Tag Archives: Statistics & Econometrics

Table 2 Fallacy (student stuff) .[embedded content]

The gender pay gap is a fact that, sad to say, to a non-negligible extent is the result of discrimination. And even though many women are not deliberately discriminated against, but rather self-select into lower-wage jobs, this in no way magically explains away the discrimination gap. As decades of socialization research has shown, women may be ‘structural’ victims of impersonal social mechanisms that in different ways aggrieve them. Wage discrimination is unacceptable. Wage...

Extreme events and how to live with them .[embedded content]

“Mediation analysis” is this thing where you have a treatment and an outcome and you’re trying to model how the treatment works: how much does it directly affect the outcome, and how much is the effect “mediated” through intermediate variables … In the real world, it’s my impression that almost all the mediation analyses that people actually fit in the social and medical sciences are misguided: lots of examples where the assumptions aren’t clear and where, in any case,...

Causality and the need to reform the teaching of statistics I will argue that realistic and thus scientifically relevant statistical theory is best viewed as a subdomain of causality theory, not a separate entity or an extension of probability. In particular, the application of statistics (and indeed most technology) must deal with causation if it is to represent adequately the underlying reality of how we came to observe what was seen … The network we...

.[embedded content] A great set of lectures — but yours truly still warns his students that regression-based averages is something we have reasons to be cautious about. Suppose we want to estimate the average causal effect of a dummy variable (T) on an observed outcome variable (O). In a usual regression context one would apply an ordinary least squares estimator (OLS) in trying to get an unbiased and consistent estimate: O = α + βT + ε, where α is a constant intercept, β a...

Why data alone does not answer counterfactual questions. .[embedded content]

What are the key assumptions of linear regression models? In Andrew Gelman’s and Jennifer Hill’s Data Analysis Using Regression and Multilevel/Hierarchical Models the authors list the assumptions of the linear regression model. The assumptions — in decreasing order of importance — are: 1. Validity. Most importantly, the data you are analyzing should map to the research question you are trying to answer. This sounds obvious but is often overlooked or ignored...

Does smoking — really — help you fight COVID-19? .[embedded content]

Counterfactual modelling (student stuff) .[embedded content]