We then use a labeled forest cover data from high-resolution imagery. When comparing the ML predictions to ground-truth labels, a naive model under-estimates forest cover near roads. Our adversarial model, by contrast, recovers unbiased estimates, giving more reliable coefficients.
We then use a labeled forest cover data from high-resolution imagery. When comparing the ML predictions to ground-truth labels, a naive model under-estimates forest cover near roads. Our adversarial model, by contrast, recovers unbiased estimates, giving more reliable coefficients.
We induce measurement error bias in a simulation of the effect of roads on forest cover. We show that a naive model yields biased estimates of this relationship, while an adversarial model gets it right.
We induce measurement error bias in a simulation of the effect of roads on forest cover. We show that a naive model yields biased estimates of this relationship, while an adversarial model gets it right.
We also introduce a simple bias test: regress the ML prediction errors on your independent variable. If nonzero, you have measurement error bias. If you run that test while gathering ground-truth data, you can estimate how many labeled observations you’ll need to reject a target amount of bias.
We also introduce a simple bias test: regress the ML prediction errors on your independent variable. If nonzero, you have measurement error bias. If you run that test while gathering ground-truth data, you can estimate how many labeled observations you’ll need to reject a target amount of bias.
Here’s how: a primary model predicts the outcome, while an adversarial model tries to predict the treatment using the prediction errors. As the adversary learns how to predict treatment, the primary model learns to make predictions where the errors contain no information about the treatment.
Here’s how: a primary model predicts the outcome, while an adversarial model tries to predict the treatment using the prediction errors. As the adversary learns how to predict treatment, the primary model learns to make predictions where the errors contain no information about the treatment.
From www.theguardian.com/us-news/2025...
From www.theguardian.com/us-news/2025...
#poliscibakes
#poliscibakes