by The LATE approach — a critique One of the reasons Guido Imbens and Joshua Angrist won the 2021 ‘Nobel prize’ in economics is their LATE approach used especially in instrumental variables estimation of causal effects. Another prominent ‘Nobel prize’ winner in economics — Angus Deaton — is not overly impressed: Without explicit prior consideration of the effect of the instrument choice on the parameter being estimated, such a procedure is effectively the opposite of standard statistical practice in which a parameter of interest is defined first, followed by an estimator that delivers that parameter. Instead, we have a procedure in which the choice of the instrument, which is guided by criteria designed for a situation in which there is no heterogeneity,
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Lars Pålsson Syll considers the following as important: Statistics & Econometrics
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The LATE approach — a critique
One of the reasons Guido Imbens and Joshua Angrist won the 2021 ‘Nobel prize’ in economics is their LATE approach used especially in instrumental variables estimation of causal effects. Another prominent ‘Nobel prize’ winner in economics — Angus Deaton — is not overly impressed:
Without explicit prior consideration of the effect of the instrument choice on the parameter being estimated, such a procedure is effectively the opposite of standard statistical practice in which a parameter of interest is defined first, followed by an estimator that delivers that parameter. Instead, we have a procedure in which the choice of the instrument, which is guided by criteria designed for a situation in which there is no heterogeneity, is implicitly allowed to determine the parameter of interest. This goes beyond the old story of looking for an object where the light is strong enough to see; rather, we have at least some control over the light but choose to let it fall where it may and then proclaim that whatever it illuminates is what we were looking for all along …
I find it hard to make any sense of the LATE. We are unlikely to learn much about the processes at work if we refuse to say anything about what determines (the effect ‘parameter’) θ; heterogeneity is not a technical problem calling for an econometric solution but a reflection of the fact that we have not started on our proper business, which is trying to understand what is going on. Of course, if we are as skeptical of the ability of economic theory to deliver useful models as are many applied economists today, the ability to avoid modeling can be seen as an advantage, though it should not be a surprise when such an approach delivers answers that are hard to interpret.
Even if we accept the limitation of only being able to say something about (some kind of) average treatment effects when using instrumental-variables designs, another significant and major problem is that researchers who use these randomization-based research strategies often set up problem formulations that are not at all the ones we really want answers to, in order to achieve ‘exact’ and ‘precise’ results. Design becomes the main thing, and as long as one can get more or less clever experiments in place, they believe they can draw far-reaching conclusions about both causality and the ability to generalize experimental outcomes to larger populations. Unfortunately, this often means that this type of research has a negative bias away from interesting and important problems towards prioritizing method selection. Design and research planning are important, but the credibility of research ultimately lies in being able to provide answers to relevant questions that both citizens and researchers want answers to. Focusing on finding narrow LATE results threatens to lead research away from the really important research questions we as social scientists want to answer.
Believing there is only one really good evidence-based method on the market — and that randomization is the only way to achieve scientific validity — blinds people to searching for and using other methods that in many contexts are better. Insisting on using only one tool often means using the wrong tool.