Cover Image

Safe and Interpretable Estimation of Optimal Treatment Regimes

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Parikh, Harsh, Lanners, Quinn, Akras, Zade, Zafar, Sahar F., Westover, M. Brandon, Rudin, Cynthia, Volfovsky, Alexander
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Applications Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Data Fusion for Partial Identification of Causal Effects
by: Lanners, Quinn, et al.
Published: (2025)

Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data
by: Katta, Srikar, et al.
Published: (2023)

A Double Machine Learning Approach to Combining Experimental and Observational Data
by: Parikh, Harsh, et al.
Published: (2023)

Who Are We Missing? A Principled Approach to Characterizing the Underrepresented Population
by: Parikh, Harsh, et al.
Published: (2024)

A Design-based Solution for Causal Inference with Text: Can a Language Model Be Too Large?
by: Tierney, Graham, et al.
Published: (2025)

Precision Mental Health: Predicting Heterogeneous Treatment Effects for Depression through Data Integration
by: Brantner, Carly L., et al.
Published: (2025)

TEA-Time: Transporting Effects Across Time
by: Parikh, Harsh, et al.
Published: (2026)

Estimating Interpretable Heterogeneous Treatment Effect with Causal Subgroup Discovery in Survival Outcomes
by: Bo, Na, et al.
Published: (2024)

Mind the Sim-to-Real Gap & Think Like a Scientist
by: Parikh, Harsh, et al.
Published: (2026)

Double Machine Learning Methods for Estimating Average Treatment Effects: A Comparative Study
by: Tan, Xiaoqing, et al.
Published: (2022)

Individualized Multi-Treatment Response Curves Estimation using RBF-net with Shared Neurons
by: Chang, Peter, et al.
Published: (2024)

Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data
by: Zeng, Cheng, et al.
Published: (2025)

A Large Scale Heterogeneous Treatment Effect Estimation Framework and Its Applications of Users' Journey at Snap
by: Pan, Jing, et al.
Published: (2025)

Doubly-Robust Bayesian Estimation of Optimal Individualized Treatment Rules using Network Meta-Analysis
by: Wigle, Augustine, et al.
Published: (2026)

Improving the Estimation of Lifetime Effects in A/B Testing via Treatment Locality
by: Chen, Shuze, et al.
Published: (2024)

Constructing a T-test for Value Function Comparison of Individualized Treatment Regimes in the Presence of Multiple Imputation for Missing Data
by: Lu, Minxin, et al.
Published: (2023)

On Model-Based Clustering With Entropic Optimal Transport
by: Mena, Gonzalo
Published: (2026)

Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals
by: Arpogaus, Marcel, et al.
Published: (2025)

Fusing Individualized Treatment Rules Using Secondary Outcomes
by: Gao, Daiqi, et al.
Published: (2024)

Estimating Treatment Effect under Additive Hazards Models with High-dimensional Covariates
by: Hou, Jue, et al.
Published: (2019)

Efficient Generative Modeling via Penalized Optimal Transport Network
by: Lu, Wenhui Sophia, et al.
Published: (2024)

Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions
by: Deliu, Nina, et al.
Published: (2022)

A Bayesian Classification Trees Approach to Treatment Effect Variation with Noncompliance
by: Fisher, Jared D., et al.
Published: (2024)

Adaptive Selection of the Optimal Strategy to Improve Precision and Power in Randomized Trials
by: Balzer, Laura B., et al.
Published: (2022)

Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
by: Imai, Kosuke, et al.
Published: (2022)

Group-Aware Matrix Estimation and Latent Subspace Recovery
by: Golubovic, Hamza, et al.
Published: (2026)

Performance of Cross-Validated Targeted Maximum Likelihood Estimation
by: Smith, Matthew J., et al.
Published: (2024)

A Honest Cross-Validation Estimator for Prediction Performance
by: Pan, Tianyu, et al.
Published: (2025)

A Causal Machine Learning Framework for Treatment Personalization in Clinical Trials: Application to Ulcerative Colitis
by: Minoccheri, Cristian, et al.
Published: (2026)

Targeted Maximum Likelihood Estimation for Integral Projection Models in Population Ecology
by: Zhou, Yunzhe, et al.
Published: (2024)

Fast Uncertainty Quantification for Kernel-Based Estimators in Large-Scale Causal Inference
by: Kosko, Matthew, et al.
Published: (2026)

A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation
by: Song, Xinran, et al.
Published: (2025)

Estimating Conditional Average Treatment Effects with Heteroscedasticity by Model Averaging and Matching
by: Shi, Pengfei, et al.
Published: (2023)

Exploring Learning Rate Selection in Generalised Bayesian Inference using Posterior Predictive Checks
by: Zafar, Schyan, et al.
Published: (2024)

Likelihood-Free Estimation for Spatiotemporal Hawkes processes with missing data and application to predictive policing
by: Das, Pramit, et al.
Published: (2025)

Bayesian Fractional Polynomials for Optimal Dosage Estimation with Fish Nutrition Applications
by: Hubin, Aliaksandr, et al.
Published: (2026)

Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis
by: Goplerud, Max, et al.
Published: (2022)

Shifting the Paradigm: Estimating Heterogeneous Treatment Effects in the Development of Walkable Cities Design
by: Zhu, Jie, et al.
Published: (2024)

Model-Twin Randomization (MoTR) for Estimating the Recurring Individual Treatment Effect
by: Daza, Eric J., et al.
Published: (2022)

Causal Feature Learning in the Social Sciences
by: Huang, Jingzhou, et al.
Published: (2025)