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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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
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 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.
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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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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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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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