Saved in:
| Main Authors: | Lan, Hui, Chang, Haoge, Dillon, Eleanor, Syrgkanis, Vasilis |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.04699 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Causal Q-Aggregation for CATE Model Selection
by: Lan, Hui, et al.
Published: (2023)
by: Lan, Hui, et al.
Published: (2023)
Learning Treatment Representations for Downstream Instrumental Variable Regression
by: Lin, Shiangyi, et al.
Published: (2025)
by: Lin, Shiangyi, et al.
Published: (2025)
Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation
by: Mahajan, Divyat, et al.
Published: (2022)
by: Mahajan, Divyat, et al.
Published: (2022)
Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation
by: Jin, Jikai, et al.
Published: (2024)
by: Jin, Jikai, et al.
Published: (2024)
Dynamic Local Average Treatment Effects
by: Sojitra, Ravi B., et al.
Published: (2024)
by: Sojitra, Ravi B., et al.
Published: (2024)
Direct Preference Optimization With Unobserved Preference Heterogeneity: The Necessity of Ternary Preferences
by: Chidambaram, Keertana, et al.
Published: (2024)
by: Chidambaram, Keertana, et al.
Published: (2024)
Adaptive Estimation and Inference in Conditional Moment Models via the Discrepancy Principle
by: Tan, Jiyuan, et al.
Published: (2026)
by: Tan, Jiyuan, et al.
Published: (2026)
Post Reinforcement Learning Inference
by: Syrgkanis, Vasilis, et al.
Published: (2023)
by: Syrgkanis, Vasilis, et al.
Published: (2023)
The Partial Testimony of Logs: Evaluation of Language Model Generation under Confounded Model Choice
by: Jin, Jikai, et al.
Published: (2026)
by: Jin, Jikai, et al.
Published: (2026)
Estimation of Treatment Effects in Extreme and Unobserved Data
by: Tan, Jiyuan, et al.
Published: (2025)
by: Tan, Jiyuan, et al.
Published: (2025)
Sharp Structure-Agnostic Lower Bounds for General Linear Functional Estimation
by: Jin, Jikai, et al.
Published: (2025)
by: Jin, Jikai, et al.
Published: (2025)
Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
by: Jin, Jikai, et al.
Published: (2023)
by: Jin, Jikai, et al.
Published: (2023)
Regularized DeepIV with Model Selection
by: Li, Zihao, et al.
Published: (2024)
by: Li, Zihao, et al.
Published: (2024)
Order-Explicit Linearization of High-Dimensional $U$-Statistics
by: Ritzwoller, David M., et al.
Published: (2024)
by: Ritzwoller, David M., et al.
Published: (2024)
Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity
by: Balazadeh, Vahid, et al.
Published: (2024)
by: Balazadeh, Vahid, et al.
Published: (2024)
Detecting clinician implicit biases in diagnoses using proximal causal inference
by: Liu, Kara, et al.
Published: (2025)
by: Liu, Kara, et al.
Published: (2025)
Consistency of Neural Causal Partial Identification
by: Tan, Jiyuan, et al.
Published: (2024)
by: Tan, Jiyuan, et al.
Published: (2024)
Statistical Inference and Learning for Shapley Additive Explanations (SHAP)
by: Whitehouse, Justin, et al.
Published: (2026)
by: Whitehouse, Justin, et al.
Published: (2026)
Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects
by: Liang, Zhongyuan, et al.
Published: (2025)
by: Liang, Zhongyuan, et al.
Published: (2025)
Preference Learning with Response Time: Robust Losses and Guarantees
by: Sawarni, Ayush, et al.
Published: (2025)
by: Sawarni, Ayush, et al.
Published: (2025)
It's Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation
by: Jin, Jikai, et al.
Published: (2025)
by: Jin, Jikai, et al.
Published: (2025)
Taking a Moment for Distributional Robustness
by: Hastings, Jabari, et al.
Published: (2024)
by: Hastings, Jabari, et al.
Published: (2024)
Synthetic Blips: Generalizing Synthetic Controls for Dynamic Treatment Effects
by: Agarwal, Anish, et al.
Published: (2022)
by: Agarwal, Anish, et al.
Published: (2022)
Policy Learning with Abstention
by: Sawarni, Ayush, et al.
Published: (2025)
by: Sawarni, Ayush, et al.
Published: (2025)
Adversarial Estimation of Riesz Representers
by: Chernozhukov, Victor, et al.
Published: (2020)
by: Chernozhukov, Victor, et al.
Published: (2020)
Towards efficient representation identification in supervised learning
by: Ahuja, Kartik, et al.
Published: (2022)
by: Ahuja, Kartik, et al.
Published: (2022)
Inference on Optimal Policy Values and Other Irregular Functionals via Softmax Smoothing
by: Whitehouse, Justin, et al.
Published: (2025)
by: Whitehouse, Justin, et al.
Published: (2025)
Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning
by: Jin, Jikai, et al.
Published: (2025)
by: Jin, Jikai, et al.
Published: (2025)
Prescriptive Scaling Reveals the Evolution of Language Model Capabilities
by: Zhang, Hanlin, et al.
Published: (2026)
by: Zhang, Hanlin, et al.
Published: (2026)
Automatic Doubly Robust Forests
by: Chen, Zhaomeng, et al.
Published: (2024)
by: Chen, Zhaomeng, et al.
Published: (2024)
Applied Causal Inference Powered by ML and AI
by: Chernozhukov, Victor, et al.
Published: (2024)
by: Chernozhukov, Victor, et al.
Published: (2024)
Predicting Long Term Sequential Policy Value Using Softer Surrogates
by: Nam, Hyunji, et al.
Published: (2024)
by: Nam, Hyunji, et al.
Published: (2024)
Long Story Short: Omitted Variable Bias in Causal Machine Learning
by: Chernozhukov, Victor, et al.
Published: (2021)
by: Chernozhukov, Victor, et al.
Published: (2021)
Orthogonal Causal Calibration
by: Whitehouse, Justin, et al.
Published: (2024)
by: Whitehouse, Justin, et al.
Published: (2024)
Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration
by: Ngo, Daniel, et al.
Published: (2023)
by: Ngo, Daniel, et al.
Published: (2023)
Personalized Adaptation via In-Context Preference Learning
by: Lau, Allison, et al.
Published: (2024)
by: Lau, Allison, et al.
Published: (2024)
Conformal Convolution and Monte Carlo Meta-learners for Predictive Inference of Individual Treatment Effects
by: Jonkers, Jef, et al.
Published: (2024)
by: Jonkers, Jef, et al.
Published: (2024)
M-learner:A Flexible And Powerful Framework To Study Heterogeneous Treatment Effect In Mediation Model
by: Li, Xingyu, et al.
Published: (2025)
by: Li, Xingyu, et al.
Published: (2025)
Beyond Differences: Doubly Robust Meta-Learners for Ratio-Based Treatment Effects
by: Fuchs, Michael, et al.
Published: (2026)
by: Fuchs, Michael, et al.
Published: (2026)
Meta-Learning and representation learner: A short theoretical note
by: Bouchattaoui, Mouad El
Published: (2024)
by: Bouchattaoui, Mouad El
Published: (2024)
Similar Items
-
Causal Q-Aggregation for CATE Model Selection
by: Lan, Hui, et al.
Published: (2023) -
Learning Treatment Representations for Downstream Instrumental Variable Regression
by: Lin, Shiangyi, et al.
Published: (2025) -
Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation
by: Mahajan, Divyat, et al.
Published: (2022) -
Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation
by: Jin, Jikai, et al.
Published: (2024) -
Dynamic Local Average Treatment Effects
by: Sojitra, Ravi B., et al.
Published: (2024)