Framework using potential outcomes and within-treatment regression models to estimate plot-level SOC sequestration potentials from covariates and approximate optimal policies, demonstrated on California rangeland data where targeting low-baseline-SOC plots improves outcomes over uniform policies.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Including LLM predictions as covariates in standard regression adjustment for randomized experiments reduces variance with a do-no-harm property that reverts to the unadjusted estimator when predictions are uninformative.
citing papers explorer
-
Estimating soil carbon sequestration potential and approximating optimal management policies
Framework using potential outcomes and within-treatment regression models to estimate plot-level SOC sequestration potentials from covariates and approximate optimal policies, demonstrated on California rangeland data where targeting low-baseline-SOC plots improves outcomes over uniform policies.
-
AI-Assisted Variance Reduction in Randomized Experiments
Including LLM predictions as covariates in standard regression adjustment for randomized experiments reduces variance with a do-no-harm property that reverts to the unadjusted estimator when predictions are uninformative.