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Parallel Trends and Dynamic Choices
Difference-in-differences is a common method for estimating treatment effects, and the parallel trends condition is its main identifying assumption: the trend in mean untreated outcomes is independent of the observed treatment status.
Calibrating for Class Weights by Modeling Machine Learning
A much studied issue is the extent to which the confidence scores provided by machine learning algorithms are calibrated to ground truth probabilities.
Sharp Bounds in the Latent Index Selection Model
The sharp bounds use the full content of identified marginal distributions, and analytic derivations rely on the theory of stochastic orders.
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