Saved in:
| Main Author: | Mirthipati, Tejas |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.09076 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Optimizing CNN Architectures for Advanced Thoracic Disease Classification
by: Mirthipati, Tejas
Published: (2025)
by: Mirthipati, Tejas
Published: (2025)
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)
Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model
by: Gao, Chenyin, et al.
Published: (2024)
by: Gao, Chenyin, et al.
Published: (2024)
Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis
by: Ham, Dae Woong, et al.
Published: (2022)
by: Ham, Dae Woong, et al.
Published: (2022)
Causal Decomposition Analysis with Synergistic Interventions: A Triply-Robust Machine Learning Approach to Addressing Multiple Dimensions of Social Disparities
by: Park, Soojin, et al.
Published: (2025)
by: Park, Soojin, et al.
Published: (2025)
Causal Machine Learning: A Survey and Open Problems
by: Kaddour, Jean, et al.
Published: (2022)
by: Kaddour, Jean, et al.
Published: (2022)
A Meta-Learning Approach to Bayesian Causal Discovery
by: Dhir, Anish, et al.
Published: (2024)
by: Dhir, Anish, et al.
Published: (2024)
What if? Causal Machine Learning in Supply Chain Risk Management
by: Wyrembek, Mateusz, et al.
Published: (2024)
by: Wyrembek, Mateusz, et al.
Published: (2024)
A Semiparametric Approach to Causal Inference
by: Zhang, Archer Gong, et al.
Published: (2024)
by: Zhang, Archer Gong, et al.
Published: (2024)
Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
by: Fuhr, Jonathan, et al.
Published: (2024)
by: Fuhr, Jonathan, et al.
Published: (2024)
Invariant Causal Set Covering Machines
by: Godon, Thibaud, et al.
Published: (2023)
by: Godon, Thibaud, et al.
Published: (2023)
Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules
by: Li, Michael Lingzhi, et al.
Published: (2024)
by: Li, Michael Lingzhi, et al.
Published: (2024)
Double Machine Learning for Conditional Moment Restrictions: IV Regression, Proximal Causal Learning and Beyond
by: Shao, Daqian, et al.
Published: (2025)
by: Shao, Daqian, et al.
Published: (2025)
Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments
by: Zhu, Hongtao, et al.
Published: (2024)
by: Zhu, Hongtao, et al.
Published: (2024)
A New Causal Rule Learning Approach to Interpretable Estimation of Heterogeneous Treatment Effect
by: Wu, Ying, et al.
Published: (2023)
by: Wu, Ying, et al.
Published: (2023)
Causal Mediation Analysis with Multiple Mediators: A Simulation Approach
by: Zhou, Jesse, et al.
Published: (2025)
by: Zhou, Jesse, et al.
Published: (2025)
Causal Machine Learning for Surgical Interventions
by: Tamo, J. Ben, et al.
Published: (2025)
by: Tamo, J. Ben, et al.
Published: (2025)
Multi-modal Causal Structure Learning and Root Cause Analysis
by: Zheng, Lecheng, et al.
Published: (2024)
by: Zheng, Lecheng, et al.
Published: (2024)
The Missing Link: Allocation Performance in Causal Machine Learning
by: Fischer-Abaigar, Unai, et al.
Published: (2024)
by: Fischer-Abaigar, Unai, et al.
Published: (2024)
Interpretable Machine Learning for Survival Analysis
by: Langbein, Sophie Hanna, et al.
Published: (2024)
by: Langbein, Sophie Hanna, et al.
Published: (2024)
GLACIAL: Granger and Learning-based Causality Analysis for Longitudinal Imaging Studies
by: Nguyen, Minh, et al.
Published: (2022)
by: Nguyen, Minh, et al.
Published: (2022)
A Sensitivity Approach to Causal Inference Under Limited Overlap
by: Ma, Yuanzhe, et al.
Published: (2025)
by: Ma, Yuanzhe, et al.
Published: (2025)
Causality-driven Sequence Segmentation for Enhancing Multiphase Industrial Process Data Analysis and Soft Sensing
by: He, Yimeng, et al.
Published: (2024)
by: He, Yimeng, et al.
Published: (2024)
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)
Self-Labeling in Multivariate Causality and Quantification for Adaptive Machine Learning
by: Ren, Yutian, et al.
Published: (2024)
by: Ren, Yutian, et al.
Published: (2024)
DCILP: A Distributed Approach for Large-Scale Causal Structure Learning
by: Dong, Shuyu, et al.
Published: (2024)
by: Dong, Shuyu, et al.
Published: (2024)
Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning
by: Wienöbst, Marcel, et al.
Published: (2025)
by: Wienöbst, Marcel, et al.
Published: (2025)
Sequential Transport for Causal Mediation Analysis
by: Machado, Agathe Fernandes, et al.
Published: (2026)
by: Machado, Agathe Fernandes, et al.
Published: (2026)
Discrete Causal Representation Learning
by: Zhang, Wenjin, et al.
Published: (2026)
by: Zhang, Wenjin, et al.
Published: (2026)
A Doubly Robust Machine Learning Approach for Disentangling Treatment Effect Heterogeneity with Functional Outcomes
by: Salmaso, Filippo, et al.
Published: (2026)
by: Salmaso, Filippo, et al.
Published: (2026)
Causal Flow-based Variational Auto-Encoder for Disentangled Causal Representation Learning
by: Fan, Di, et al.
Published: (2023)
by: Fan, Di, et al.
Published: (2023)
Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach
by: Takayama, Masayuki, et al.
Published: (2024)
by: Takayama, Masayuki, et al.
Published: (2024)
Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption
by: Poinsot, Audrey, et al.
Published: (2025)
by: Poinsot, Audrey, et al.
Published: (2025)
Impact of Physical Activity on Quality of Life During Pregnancy: A Causal ML Approach
by: Kazemi, Kianoosh, et al.
Published: (2024)
by: Kazemi, Kianoosh, et al.
Published: (2024)
Neural Networks with Causal Graph Constraints: A New Approach for Treatment Effects Estimation
by: Pros, Roger, et al.
Published: (2024)
by: Pros, Roger, et al.
Published: (2024)
Horseshoe Forests for High-Dimensional Causal Survival Analysis
by: Jacobs, Tijn, et al.
Published: (2025)
by: Jacobs, Tijn, et al.
Published: (2025)
Adjustment Identification Distance: A gadjid for Causal Structure Learning
by: Henckel, Leonard, et al.
Published: (2024)
by: Henckel, Leonard, et al.
Published: (2024)
A Causal Analysis of CO2 Reduction Strategies in Electricity Markets Through Machine Learning-Driven Metalearners
by: Naeini, Iman Emtiazi, et al.
Published: (2024)
by: Naeini, Iman Emtiazi, et al.
Published: (2024)
Causally-Aware Unsupervised Feature Selection Learning
by: Shen, Zongxin, et al.
Published: (2024)
by: Shen, Zongxin, et al.
Published: (2024)
Causal Effect Estimation with Learned Instrument Representations
by: Dean, Frances, et al.
Published: (2026)
by: Dean, Frances, et al.
Published: (2026)
Similar Items
-
Optimizing CNN Architectures for Advanced Thoracic Disease Classification
by: Mirthipati, Tejas
Published: (2025) -
Double Machine Learning at Scale to Predict Causal Impact of Customer Actions
by: More, Sushant, et al.
Published: (2024) -
Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model
by: Gao, Chenyin, et al.
Published: (2024) -
Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis
by: Ham, Dae Woong, et al.
Published: (2022) -
Causal Decomposition Analysis with Synergistic Interventions: A Triply-Robust Machine Learning Approach to Addressing Multiple Dimensions of Social Disparities
by: Park, Soojin, et al.
Published: (2025)