Record Details

Replication Data for: Recalibration Of Predicted Probabilities Using the "Logit Shift": Why does it work, and when can it be expected to work well?

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

View Archive Info
 
 
Field Value
 
Title Replication Data for: Recalibration Of Predicted Probabilities Using the "Logit Shift": Why does it work, and when can it be expected to work well?
 
Identifier https://doi.org/10.7910/DVN/7MRDUW
 
Creator Rosenman, Evan T. R.
McCartan, Cory
Olivella, Santiago
 
Publisher Harvard Dataverse
 
Description The output of predictive models is routinely recalibrated by reconciling low-level predictions with known quantities defined at higher levels of aggregation. For example, models predicting vote probabilities at the individual level in U.S. elections can be adjusted so that their aggregation matches the observed vote totals in each county, thus producing better calibrated predictions. In this research note, we provide theoretical grounding for one of the most commonly used recalibration strategies, known colloquially as the "logit shift." Typically cast as a heuristic adjustment strategy (whereby a constant correction on the logit scale is found, such that aggregated predictions match target totals), we show that the logit shift offers a fast and accurate approximation to a principled, but computationally impractical adjustment strategy: computing the posterior prediction probabilities, conditional on the observed totals. After deriving analytical bounds on the quality of the approximation, we illustrate its accuracy using Monte Carlo simulations. We also discuss scenarios in which the logit shift is less effective at recalibrating predictions: when the target totals are defined only for highly heterogeneous populations, and when the original predictions correctly capture the mean of true individual probabilities, but fail to capture the shape of their distribution.
 
Subject Social Sciences
 
Contributor Olivella, Santiago