by .[embedded content] Great presentation, but I do think Angrist ought to have also mentioned that although ‘ideally controlled experiments’ may tell us with certainty what causes what effects, this is so only when given the right ‘closures.’ Making appropriate extrapolations from — ideal, accidental, natural or quasi — experiments to different settings, populations or target systems, is not easy. “It works there” is no evidence for “it will work here.” The causal background assumptions made have to be justified, and without licenses to export, the value of ‘rigorous’ and ‘precise’ methods used when analyzing ‘natural experiments’ is often despairingly small. Since the core assumptions on which IV analysis builds are NEVER directly testable, those of us who choose to use
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Lars Pålsson Syll considers the following as important: Statistics & Econometrics
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Great presentation, but I do think Angrist ought to have also mentioned that although ‘ideally controlled experiments’ may tell us with certainty what causes what effects, this is so only when given the right ‘closures.’ Making appropriate extrapolations from — ideal, accidental, natural or quasi — experiments to different settings, populations or target systems, is not easy. “It works there” is no evidence for “it will work here.” The causal background assumptions made have to be justified, and without licenses to export, the value of ‘rigorous’ and ‘precise’ methods used when analyzing ‘natural experiments’ is often despairingly small. Since the core assumptions on which IV analysis builds are NEVER directly testable, those of us who choose to use instrumental variables to find out about causality ALWAYS have to defend and argue for the validity of the assumptions the causal inferences build on. Especially when dealing with natural experiments, we should be very cautious when being presented with causal conclusions without convincing arguments about the veracity of the assumptions made. If you are out to make causal inferences you have to rely on a trustworthy theory of the data generating process. The empirical results causal analysis supply us with are only as good as the assumptions we make about the data generating process. Garbage in, garbage out.