Heat-kernel smoothing over weighted points on a compact manifold yields a scale-dependent geometric effective sample size that discounts nearby and duplicate particles.
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5 Pith papers cite this work, alongside 17 external citations. Polarity classification is still indexing.
5
Pith papers citing it
17
external citations · OpenAlex
representative citing papers
Introduces a transportability-based approach to model population-level exposure effects as a function of effect modifier prevalences for heterogeneity analysis.
Unified targeted regularization framework for causal effect estimation with EDF outcomes using neural networks that jointly estimate outcome model, propensity scores, and fluctuation parameter.
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.
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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Heat-Kernel Entropy Profiles and Geometric Effective Sample Size for Weighted Measures on Manifolds
Heat-kernel smoothing over weighted points on a compact manifold yields a scale-dependent geometric effective sample size that discounts nearby and duplicate particles.
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From Subgroups to Population Composition: A Transportability Approach to Effect Heterogeneity
Introduces a transportability-based approach to model population-level exposure effects as a function of effect modifier prevalences for heterogeneity analysis.
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Targeted Regularization for Causal Effect Estimation with Exponential Dispersion Family Outcomes
Unified targeted regularization framework for causal effect estimation with EDF outcomes using neural networks that jointly estimate outcome model, propensity scores, and fluctuation parameter.
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A Conformal Selection Framework for Individual Treatment Beneficiaries with Auxiliary External Data
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.
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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.