Saved in:
| Main Author: | Vowels, Matthew J. |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2308.04365 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutants
by: Xu, Gang, et al.
Published: (2024)
by: Xu, Gang, et al.
Published: (2024)
Fast Uncertainty Quantification for Kernel-Based Estimators in Large-Scale Causal Inference
by: Kosko, Matthew, et al.
Published: (2026)
by: Kosko, Matthew, et al.
Published: (2026)
Testing Generalizability in Causal Inference
by: Manela, Daniel de Vassimon, et al.
Published: (2024)
by: Manela, Daniel de Vassimon, et al.
Published: (2024)
Modelling wildland fire burn severity in California using a spatial Super Learner approach
by: Simafranca, Nicholas, et al.
Published: (2023)
by: Simafranca, Nicholas, et al.
Published: (2023)
Causal Machine Learning for Surgical Interventions
by: Tamo, J. Ben, et al.
Published: (2025)
by: Tamo, J. Ben, et al.
Published: (2025)
Hybrid$^2$ Neural ODE Causal Modeling and an Application to Glycemic Response
by: Zou, Bob Junyi, et al.
Published: (2024)
by: Zou, Bob Junyi, et al.
Published: (2024)
Double Machine Learning for Adaptive Causal Representation in High-Dimensional Data
by: Aouar, Lynda, et al.
Published: (2024)
by: Aouar, Lynda, et al.
Published: (2024)
Explainable Federated Bayesian Causal Inference and Its Application in Advanced Manufacturing
by: Xiao, Xiaofeng, et al.
Published: (2025)
by: Xiao, Xiaofeng, et al.
Published: (2025)
Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes
by: Du, Jin-Hong, et al.
Published: (2024)
by: Du, Jin-Hong, et al.
Published: (2024)
Advancing Causal Inference: A Nonparametric Approach to ATE and CATE Estimation with Continuous Treatments
by: Souto, Hugo Gobato, et al.
Published: (2024)
by: Souto, Hugo Gobato, et al.
Published: (2024)
Double Machine Learning at Scale to Predict Causal Impact of Customer Actions
by: More, Sushant, et al.
Published: (2024)
by: More, Sushant, et al.
Published: (2024)
Bridging the Gap Between Climate Science and Machine Learning in Climate Model Emulation
by: Schmidt, Luca, et al.
Published: (2026)
by: Schmidt, Luca, et al.
Published: (2026)
A Causal Machine Learning Framework for Treatment Personalization in Clinical Trials: Application to Ulcerative Colitis
by: Minoccheri, Cristian, et al.
Published: (2026)
by: Minoccheri, Cristian, et al.
Published: (2026)
Balancing Interpretability and Flexibility in Modeling Diagnostic Trajectories with an Embedded Neural Hawkes Process Model
by: Zhao, Yuankang, et al.
Published: (2025)
by: Zhao, Yuankang, et al.
Published: (2025)
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
by: Imai, Kosuke, et al.
Published: (2022)
by: Imai, Kosuke, et al.
Published: (2022)
Model-Agnostic Interpretation Framework in Machine Learning: A Comparative Study in NBA Sports
by: Liu, Shun
Published: (2024)
by: Liu, Shun
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)
by: Tierney, Graham, et al.
Published: (2025)
bioLeak: Leakage-Aware Modeling and Diagnostics for Machine Learning in R
by: Korkmaz, Selçuk
Published: (2026)
by: Korkmaz, Selçuk
Published: (2026)
A State-Space Perspective on Modelling and Inference for Online Skill Rating
by: Duffield, Samuel, et al.
Published: (2023)
by: Duffield, Samuel, et al.
Published: (2023)
Predicting VBAC Outcomes from U.S. Natality Data using Deep and Classical Machine Learning Models
by: Anand, Ananya
Published: (2025)
by: Anand, Ananya
Published: (2025)
Age-Dependent Heterogeneity in the Association Between Physical Activity and Mental Distress: A Causal Machine Learning Analysis of 3.2 Million U.S. Adults
by: Shan, Yuan
Published: (2026)
by: Shan, Yuan
Published: (2026)
Efficient Causal Structure Learning via Modular Subgraph Integration
by: Sun, Haixiang, et al.
Published: (2026)
by: Sun, Haixiang, et al.
Published: (2026)
On the Role of Surrogates in Conformal Inference of Individual Causal Effects
by: Gao, Chenyin, et al.
Published: (2024)
by: Gao, Chenyin, et al.
Published: (2024)
Causal Feature Learning in the Social Sciences
by: Huang, Jingzhou, et al.
Published: (2025)
by: Huang, Jingzhou, et al.
Published: (2025)
Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experiments
by: Kovačević, Luka, et al.
Published: (2024)
by: Kovačević, Luka, et al.
Published: (2024)
Testing and Improving the Robustness of Amortized Bayesian Inference for Cognitive Models
by: Wu, Yufei, et al.
Published: (2024)
by: Wu, Yufei, et al.
Published: (2024)
Valuing an Engagement Surface using a Large Scale Dynamic Causal Model
by: Mukerji, Abhimanyu, et al.
Published: (2024)
by: Mukerji, Abhimanyu, et al.
Published: (2024)
Curious Causality-Seeking Agents Learn Meta Causal World
by: Zhao, Zhiyu, et al.
Published: (2025)
by: Zhao, Zhiyu, et al.
Published: (2025)
Comprehensive Benchmarking of Machine Learning Methods for Risk Prediction Modelling from Large-Scale Survival Data: A UK Biobank Study
by: Oexner, Rafael R., et al.
Published: (2025)
by: Oexner, Rafael R., et al.
Published: (2025)
Modeling Discrete Coating Degradation Events via Hawkes Processes
by: Repasky, Matthew, et al.
Published: (2025)
by: Repasky, Matthew, et al.
Published: (2025)
Scalable Inference for Bayesian Multinomial Logistic-Normal Dynamic Linear Models
by: Saxena, Manan, et al.
Published: (2024)
by: Saxena, Manan, et al.
Published: (2024)
Prediction of Respiratory Syncytial Virus-Associated Hospitalizations Using Machine Learning Models Based on Environmental Data
by: Guo, Eric
Published: (2025)
by: Guo, Eric
Published: (2025)
Feature Group Tabular Transformer: A Novel Approach to Traffic Crash Modeling and Causality Analysis
by: Lares, Oscar, et al.
Published: (2024)
by: Lares, Oscar, et al.
Published: (2024)
Bayesian Nonlinear PDE Inference via Gaussian Process Collocation with Application to the Richards Equation
by: Yang, Yumo, et al.
Published: (2025)
by: Yang, Yumo, et al.
Published: (2025)
Language Models as Causal Effect Generators
by: Bynum, Lucius E. J., et al.
Published: (2024)
by: Bynum, Lucius E. J., et al.
Published: (2024)
Bayesian Hybrid Machine Learning of Gallstone Risk
by: Chakraborty, Chitradipa, et al.
Published: (2025)
by: Chakraborty, Chitradipa, et al.
Published: (2025)
A Novel Framework for Analyzing Structural Transformation in Data-Constrained Economies Using Bayesian Modeling and Machine Learning
by: Katende, Ronald
Published: (2024)
by: Katende, Ronald
Published: (2024)
Inference for Large Scale Regression Models with Dependent Errors
by: Voirol, Lionel, et al.
Published: (2024)
by: Voirol, Lionel, et al.
Published: (2024)
Generating Synthetic Relational Tabular Data via Structural Causal Models
by: Hoppe, Frederik, et al.
Published: (2025)
by: Hoppe, Frederik, et al.
Published: (2025)
Sample Efficient Bayesian Learning of Causal Graphs from Interventions
by: Zhou, Zihan, et al.
Published: (2024)
by: Zhou, Zihan, et al.
Published: (2024)
Similar Items
-
Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutants
by: Xu, Gang, et al.
Published: (2024) -
Fast Uncertainty Quantification for Kernel-Based Estimators in Large-Scale Causal Inference
by: Kosko, Matthew, et al.
Published: (2026) -
Testing Generalizability in Causal Inference
by: Manela, Daniel de Vassimon, et al.
Published: (2024) -
Modelling wildland fire burn severity in California using a spatial Super Learner approach
by: Simafranca, Nicholas, et al.
Published: (2023) -
Causal Machine Learning for Surgical Interventions
by: Tamo, J. Ben, et al.
Published: (2025)