Always, but always, plot your data. Remember that data quality is at least as important as data quantity. Always ask yourself, “Do these results make economic/common sense”? Check whether your “statistically significant” results are also “numerically/economically significant”. Be sure that you know exactly what assumptions are used/needed to obtain the results relating to the properties […]
Statistics & Econometrics
Debating econometrics and its shortcomings, yours truly often gets the response from econometricians that: “OK, maybe econometrics isn’t perfect, but you have to admit that it is a great technique for the empirical testing of economic hypotheses.” But is econometrics really such a great testing instrument? Econometrics is supposed to be able to test economic theories. But […]
Econometricians are, of course, well aware of the importance of the relationship between the data and the underlying phenomena of interest. In the literature, this relationship is generally couched in terms of a data-generating process (DGP) … If we were to be able to perceive the true DGP in its entirety, we would essentially know […]
Important questions often require a leap from the empirical evidence to the theoretical claim. Sociology stands out from other social sciences for its willingness to tackle hard questions even when they require such a leap; however, burying the estimand obscures those decisions and confuses the link between theory and evidence. At best, this creates an […]
Traditionally, analysts use data on stopped individuals to study bias by computing the difference in violence rates between stopped minority and white civilians, while controlling for observable differences between these two sets of encounters. We term this the “naïve estimator” … However, without further assumptions, this quantity will have no causal interpretation so long as […]
In earlier blog posts, yours truly has discussed the problems of confounding and ‘overcontrolling’ in causal analysis. A good illustration of how attempts to control for additional variables can sometimes worsen rather than improve causal estimates is the so-called M-bias problem. Let me give an example from economics to illustrate the issue. Estimating causal relationships […]
Judea Pearl, in his The Book of Why, discusses the problems that arise if we thoughtlessly try to ‘control’ for too much in our quest to identify causal relationships. One of his examples concerns the paradox that when we want to find out whether mothers’ smoking increases the risk of infant mortality, but only study […]
A helpful intuition for understanding ‘collider bias’ — the spurious correlation induced when one controls for a common effect rather than a common cause — can be found in modern dating behaviour. In a population at large, it is reasonable to assume that attractiveness and personality are independent. Mean people are not necessarily attractive, nor […]
. A classic textbook example of the ‘Table 2 Fallacy’ in economics arises when estimating the return to education and misinterpreting regression coefficients. Suppose an economist wishes to estimate the causal effect of an additional year of schooling on earnings and estimates If the estimated coefficient is small and statistically insignificant, the economist might conclude […]