A new optimization algorithm with double machine learning for wildfire spread estimation enables better crew assignments that reduce total area burned.
Observational studies , volume=
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Measurement error in latent confounders produces biased ATE estimates and miscalibrated intervals under conventional adjustment; a Bayesian joint model of measurement, treatment, and outcome is proposed to correct it.
citing papers explorer
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Predictive and Prescriptive AI toward Optimizing Wildfire Suppression
A new optimization algorithm with double machine learning for wildfire spread estimation enables better crew assignments that reduce total area burned.
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Measurement Induced Confounding
Measurement error in latent confounders produces biased ATE estimates and miscalibrated intervals under conventional adjustment; a Bayesian joint model of measurement, treatment, and outcome is proposed to correct it.