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As I document using evidence from a journal data repository that I manage, the datasets used in empirical work are getting larger. When we use very large datasets, it can be dangerous to rely on standard methods for statistical inference. In addition, we need to worry about computational issues....
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literature, such as (i) whether excluding group identity from use in training the algorithm is optimal and (ii) whether there are … less discriminatory alternatives to a given algorithm. In addition, we construct an estimator for the distance between a … given algorithm and the fairest point on the frontier, and characterize its asymptotic distribution. Using Monte Carlo …
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invalid in this case. Given the success of empirical econometrics, this leads to the question: Why has econometric inference …
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Parameter estimates of structural economic models are often difficult to interpret at the light of the underlying economic theory. Bayesian methods have become increasingly popular as a tool for conducting inference on structural models since priors offer a way to exert control over the...
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The fundamental problem of external validity is not to generalize from one experiment, so much as to experimentally test generalizable theories. That is, theories that explain the systematic variation of causal effects across contexts. Here we show how the graphical language of causal diagrams...
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