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.

168,742 papers · 148 categories

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10 results for “T-learner”

Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatmentsstat.ME

Study evaluates the impact of academic support center's face-to-face assistance on student performance.

problem Underestimation of Academic Support Center's true impact due to group bias.

method Applied causal inference theory and T-learner to evaluate conditional average treatment effect (CATE) of F2F personal assistance.

result Developed a new CATE function that depends on the number of F2F sessions, predicting improved CATE performance.

academic support face-to-face assistance conditional average treatment effect causal inference T-learner

2024-11-03abs ↗pdf ↗

Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regressionecon.EM

Study shows linear models can predict CATE without overfitting, even with large data.

problem Overfitting in large-scale causal inference models.

method Investigated linear models for CATE prediction, considering samples with switching distributions.

result IPW-learner converges risk to zero if propensity score is known, while T-learner fails to achieve consistency.

benign overfitting CATE prediction linear regression causal inference overparameterized models

2022-02-10abs ↗pdf ↗

Estimation of Personalized Heterogeneous Treatment Effects Using Concatenation and Augmentation of Feature Vectorsstat.ML

A new meta-algorithm for estimating the conditional average treatment effects is proposed in the paper. The main idea underlying the algorithm is to consider a new dataset consisting of feature vectors produced by means of concatenation of examples from control and treatment groups, which are close to each other. Outco…

2019-09-09abs ↗pdf ↗

Multi-CATE: Multi-Accurate Conditional Average Treatment Effect Estimation Robust to Unknown Covariate Shiftscs.LG

Method improves treatment effect prediction robust to unknown covariate shifts.

problem Estimating heterogeneous treatment effects for different populations.

method Post-processing CATE T-learners with multi-accurate predictors to handle unknown covariate shifts.

result Improves bias and mean squared error in simulations with covariate shifts.

CATE covariate shift treatment effect machine learning causal inference

2024-05-28abs ↗pdf ↗

BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effectcs.LG

BENK estimates treatment effects with neural kernels for censored data.

problem Estimating heterogeneous treatment effects with censored time-to-event data.

method Proposes a method using the Beran estimator with neural kernels for survival functions.

result Shows improved accuracy compared to existing methods in various scenarios.

treatment effect censored data neural kernels survival analysis censoring

2022-11-19abs ↗pdf ↗

The Impact of Medication Non-adherence on Adverse Outcomes: Evidence from Schizophrenia Patients via Survival Analysiscs.AI

Study finds non-adherence to schizophrenia meds leads to earlier adverse events.

problem Impact of medication non-adherence on adverse outcomes in schizophrenia patients.

method Survival analysis, causal inference methods (T-learner, S-learner, nearest neighbor matching), different amounts of longitudinal information.

result Non-adherence to schizophrenia meds advances adverse events by 1 to 4 months.

schizophrenia medication non-adherence survival analysis causal inference adverse outcomes

2025-06-22abs ↗pdf ↗

Conformal Convolution and Monte Carlo Meta-learners for Predictive Inference of Individual Treatment Effectscs.LG

Two new methods generate probabilistic forecasts of individual treatment effects.

problem Generating probabilistic forecasts of individual treatment effects for risk-aware decision-making.

method Proposes CCT and CMC meta-learners combining conformal predictive systems with analytic convolution or Monte Carlo sampling.

result Achieve probabilistically calibrated predictive distributions and performant continuous ranked probability scores.

individual treatment effects probabilistic forecasting conformal predictive systems Monte Carlo sampling covariate shift

2024-02-07abs ↗pdf ↗

Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regressioncs.LG

A new method TNW-CATE estimates treatment effects using neural networks.

problem Estimating heterogeneous treatment effects with limited controls and many treatments.

method Trainable Nadaraya-Watson regression with shared parameters neural network.

result TNW-CATE outperforms traditional methods in various simulation experiments.

treatment effect Nadaraya-Watson transfer learning neural networks control outcome

2022-07-19abs ↗pdf ↗

Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrastsstat.ML

EP-learning framework improves causal contrast estimation efficiency.

problem Estimating heterogeneous causal contrasts efficiently and stably.

method EP-learning framework combining T-learning and DR-learning.

result EP-learners are oracle-efficient and outperform competitors.

EP-learning causal contrasts T-learning DR-learning oracle efficiency

2024-02-03abs ↗pdf ↗

Significance-First Splitting: Aligning Treatment Heterogeneity Detection with Honest Estimationstat.ME

A hybrid algorithm fuses significance-based splitting with honest sample-splitting for estimating heterogeneous treatment effects.

problem Estimating heterogeneous treatment effects while maintaining valid inference.

method Significance-first splitting using a squared

t

-statistic for treatment

imes

side interaction.

result Achieves approximately 90% CI coverage at the 90% nominal level across various synthetic designs and datasets.

heterogeneous treatment effects significance-based splitting honest causal trees treatment effect modification valid inference

2026-07-04abs ↗pdf ↗