A transformer foundation model is trained on synthetic data from a novel prior over continuous-treatment data-generating processes to predict treatment-response curves via in-context learning without task-specific fine-tuning.
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6 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 6representative citing papers
A model-agnostic conformal selection method reformulates CATE-based beneficiary identification as multiple testing with RCT-calibrated p-values and FDR control, allowing external data for model training.
Causal inference framework applied to natural experiments measures coupon timing effects on engagement, shown on company onboarding data and a public retention dataset.
Backdoor-adjusted ATEs on 21,098 UK Biobank participants showed total femur BMC and BMD with the largest hip fracture risk reductions (-0.0047 per SD), and adding the top 11 phenotypes to clinical variables raised AUC to 0.842 versus FRAX 0.709.
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.
Compares six meta-learners (Cox/RSF risk models paired with elastic net/RF CATE models) via simulations differing in hazard complexity and censoring, and releases the R package crsurvlearners.
citing papers explorer
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Estimating the Effect of Timing on Coupon Effectiveness
Causal inference framework applied to natural experiments measures coupon timing effects on engagement, shown on company onboarding data and a public retention dataset.
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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.
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A Guide to Estimating Conditional Average Treatment Effects in Competing Risks Settings
Compares six meta-learners (Cox/RSF risk models paired with elastic net/RF CATE models) via simulations differing in hazard complexity and censoring, and releases the R package crsurvlearners.