Introduces bridge score as balancing score for mediator ignorability, derives sharp pointwise bounds on mediator-outcome confounding via two latent parameters, and provides benchmark and residual-budget calibration for sensitivity analysis.
Finley, and Alan E
10 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 10representative citing papers
A recursive Riesz representer-based targeted minimum loss estimation procedure unifies asymptotically efficient estimation of causal estimands such as time-varying treatment effects and mediation effects.
Opal enables private long-term memory for personal AI by decoupling reasoning to a trusted enclave with a lightweight knowledge graph and piggybacking reindexing on ORAM accesses.
A new decay-adjusted spatio-temporal model improves estimation of neglected tropical disease prevalence by explicitly accounting for the waning impact of mass drug administration in sparse survey data.
A spectral generalized covariance measure enables conditional independence testing on non-Euclidean data with uniform bootstrap validity and power guarantees under doubly robust conditions.
Differential privacy reduces algorithmic collective action effectiveness, with formal lower bounds on success probability depending on collective size and privacy parameters, plus experimental verification on neural nets.
A weighted K-means plus decision-tree pipeline learns multi-action policies from observational data and is applied to HCV treatment choices for HIV co-infected patients, finding a high-clearance subgroup and potential cost savings of CAN$3.6-4.9 million.
A random-forest surrogate for likelihood change under tree moves enables delayed-acceptance SMC that cuts expensive likelihood evaluations while preserving posterior estimates on simulated and real phylogenetic data.
A physics-constrained cGAN is trained as an image-to-image translator on remote-sensing layers to recover spatial sensitivities of urban land-use change to macroeconomic indicators via backpropagation gradients.
FASE pairs a spatiotemporal graph neural network and multivariate Hawkes process for crime prediction with a fairness-constrained linear program for patrol allocation, showing that allocation fairness holds in simulation but a 3.5 percentage point detection gap between minority and non-minority ZIPs
citing papers explorer
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Sensitivity analysis for causal mediation: bridge score, sharp sensitivity bounds, and calibration
Introduces bridge score as balancing score for mediator ignorability, derives sharp pointwise bounds on mediator-outcome confounding via two latent parameters, and provides benchmark and residual-budget calibration for sensitivity analysis.
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A Riesz Representer Perspective on Targeted Learning
A recursive Riesz representer-based targeted minimum loss estimation procedure unifies asymptotically efficient estimation of causal estimands such as time-varying treatment effects and mediation effects.
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Opal: Private Memory for Personal AI
Opal enables private long-term memory for personal AI by decoupling reasoning to a trusted enclave with a lightweight knowledge graph and piggybacking reindexing on ORAM accesses.
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A decay-adjusted spatio-temporal model to account for the impact of mass drug administration on neglected tropical disease prevalence
A new decay-adjusted spatio-temporal model improves estimation of neglected tropical disease prevalence by explicitly accounting for the waning impact of mass drug administration in sparse survey data.
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Testing Conditional Independence via the Spectral Generalized Covariance Measure: Beyond Euclidean Data
A spectral generalized covariance measure enables conditional independence testing on non-Euclidean data with uniform bootstrap validity and power guarantees under doubly robust conditions.
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Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy
Differential privacy reduces algorithmic collective action effectiveness, with formal lower bounds on success probability depending on collective size and privacy parameters, plus experimental verification on neural nets.
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Policy Learning with Observational Data: The Case of Hepatitis C Treatment for HIV/HCV Co-Infected Patients
A weighted K-means plus decision-tree pipeline learns multi-action policies from observational data and is applied to HCV treatment choices for HIV co-infected patients, finding a high-clearance subgroup and potential cost savings of CAN$3.6-4.9 million.
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Accelerating Bayesian Phylogenetic Inference via Delayed Acceptance Sequential Monte Carlo with Random Forest Surrogates
A random-forest surrogate for likelihood change under tree moves enables delayed-acceptance SMC that cuts expensive likelihood evaluations while preserving posterior estimates on simulated and real phylogenetic data.
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Spatial sensitivity analysis for urban land use prediction with physics-constrained conditional generative adversarial networks
A physics-constrained cGAN is trained as an image-to-image translator on remote-sensing layers to recover spatial sensitivities of urban land-use change to macroeconomic indicators via backpropagation gradients.
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FASE : A Fairness-Aware Spatiotemporal Event Graph Framework for Predictive Policing
FASE pairs a spatiotemporal graph neural network and multivariate Hawkes process for crime prediction with a fairness-constrained linear program for patrol allocation, showing that allocation fairness holds in simulation but a 3.5 percentage point detection gap between minority and non-minority ZIPs