Tag Archives: Statistics & Econometrics

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]

Collider bias (student stuff) .[embedded content]

How statistics can be misleading .[embedded content] From a theoretical perspective, Simpson’s paradox importantly shows that causality can never be reduced to a question of statistics or probabilities. To understand causality we always have to relate it to a specific causal structure. Statistical correlations are never enough. No structure, no causality. Simpson’s paradox is an interesting paradox in itself, but it can also highlight a deficiency in the...

Contaminated data — the case of racial discrimination .[embedded content]

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