On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,181 papers · 148 categories

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9 results for “quasi-oracle”

Causal Effect Inference for Structured Treatmentscs.LG

Estimates CATEs for structured treatments using a new decomposition method.

problem Estimating conditional average treatment effects for complex data types.

method Generalized Robinson decomposition, isolating causal estimand, arbitrary model plugging, quasi-oracle convergence guarantee.

result Demonstrates superior performance in CATE estimation compared to prior work.

CATE estimation structured treatments generalized Robinson decomposition weak condition quasi-oracle guarantee

2021-06-03abs ↗pdf ↗

Quasi-Oracle Estimation of Heterogeneous Treatment Effectsstat.ML

Develops flexible algorithms for estimating heterogeneous treatment effects.

problem Flexible estimation of heterogeneous treatment effects in observational studies.

method Two-step algorithm: 1) Estimate marginal effects and treatment propensities, 2) Optimize data-adaptive objective function.

result Achieves quasi-oracle property, leading to error bounds similar to an oracle.

heterogeneous treatment effects observational studies flexible algorithms quasi-oracle penalized regression

2017-12-13abs ↗pdf ↗

An Orthogonal Learner for Individualized Outcomes in Markov Decision Processesstat.ML

A new meta-learner improves prediction of individualized outcomes in sequential decisions.

problem Predicting individualized outcomes over long horizons in sequential decision-making.

method Developed a novel meta-learner called DRQ-learner with theoretical guarantees of orthogonality and quasi-oracle efficiency.

result DRQ-learner achieves quasi-oracle efficiency, doubly robustness, and Neyman-orthogonality.

Markov decision processes individualized outcomes meta-learner orthogonality quasi-oracle efficiency

2025-09-30abs ↗pdf ↗

GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomescs.LG

Paper introduces GDR-learners for estimating potential outcomes from observational data.

problem Lack of theoretical property of general Neyman-orthogonality in deep generative models.

method Develops flexible GDR-learners based on various deep generative models.

result GDR-learners possess quasi-oracle efficiency and rate double robustness, asymptotically optimal.

deep generative models potential outcomes Neyman-orthogonality double robustness conditional distributions

2025-09-26abs ↗pdf ↗

Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoringcs.LG

The paper addresses bias in survival analysis due to informative censoring.

problem Bias in treatment effect estimates due to informative censoring in survival analysis.

method Assumption-lean framework using partial identification to derive bounds on CATE.

result Proposes a meta-learner, SurvB-learner, to estimate bounds on CATE.

Survival analysis informative censoring heterogeneous treatment effects partial identification double-robustness

2025-10-15abs ↗pdf ↗

B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confoundingcs.LG

B-Learner provides bounds on CATE under hidden confounding risks.

problem Estimating CATE in the presence of hidden confounding.

method Adapting bounds on average treatment effect to conditional distributional treatment effects.

result B-Learner offers valid, sharp, efficient, and quasi-oracle bounds on CATE.

CATE hidden confounding sharp bounds observational studies policy implications

2023-04-20abs ↗pdf ↗

Debiased Front-Door Learners for Heterogeneous Effectsstat.ML

Debiased learners estimate heterogeneous treatment effects in observational studies.

problem Estimating heterogeneous treatment effects in observational studies with unmeasured confounders.

method Debiased Front-Door (FD) learners, FD-DR-Learner and FD-R-Learner, under specific assumptions.

result Debiased learners satisfy error bounds and stage-error decompositions, delivering reliable HTE estimates.

observational studies heterogeneous treatment effects front-door adjustment mediators unmeasured confounders

2025-09-26abs ↗pdf ↗

Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learnercs.LG

Novel method to quantify aleatoric uncertainty of treatment effects from observational data.

problem Understanding randomness in treatment effects for medical treatments.

method Partial identification and Neyman-orthogonality to quantify aleatoric uncertainty.

result Developed a novel orthogonal learner (AU-learner) for quantifying aleatoric uncertainty.

aleatoric uncertainty partial identification Neyman-orthogonality deep learning treatment effect

2024-11-05abs ↗pdf ↗

Heterogeneous Causal Learning for Effectiveness Optimization in User Marketingcs.LG

Optimizes user marketing campaigns to balance cost and effectiveness.

problem Lack of methods to optimize marketing campaigns considering cost and effectiveness.

method Proposes a treatment effect optimization algorithm using deep learning to balance cost and effectiveness.

result Demonstrates superior performance in cost-efficiency and real-world business value.

user marketing treatment effect optimization deep learning cost efficiency business metrics

2020-04-21abs ↗pdf ↗