Batch-Adaptive Causal Annotations
Fuente:
arXiv
Saved in:
| Main Authors: | Nwankwo, Ezinne, Goldkind, Lauri, Zhou, Angela |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Inference for Batched Adaptive Experiments
by: Kemper, Jan, et al.
Published: (2025)
by: Kemper, Jan, et al.
Published: (2025)
Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding
by: Rambachan, Ashesh, et al.
Published: (2022)
by: Rambachan, Ashesh, et al.
Published: (2022)
Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models
by: Zhou, Guanhao, et al.
Published: (2025)
by: Zhou, Guanhao, et al.
Published: (2025)
A Large-Scale Empirical Comparison of Meta-Learners and Causal Forests for Heterogeneous Treatment Effect Estimation in Marketing Uplift Modeling
by: Singh, Aman
Published: (2026)
by: Singh, Aman
Published: (2026)
Re-examining Granger Causality with Causal Bayesian Networks and Reichenbachs Principles
by: Adedayo, S. A.
Published: (2025)
by: Adedayo, S. A.
Published: (2025)
Causal Inference on Outcomes Learned from Text
by: Modarressi, Iman, et al.
Published: (2025)
by: Modarressi, Iman, et al.
Published: (2025)
Regularizing Extrapolation in Causal Inference
by: Arbour, David, et al.
Published: (2025)
by: Arbour, David, et al.
Published: (2025)
Multiply-Robust Causal Change Attribution
by: Quintas-Martinez, Victor, et al.
Published: (2024)
by: Quintas-Martinez, Victor, et al.
Published: (2024)
Data Fusion for Partial Identification of Causal Effects
by: Lanners, Quinn, et al.
Published: (2025)
by: Lanners, Quinn, et al.
Published: (2025)
Synthetic Potential Outcomes and Causal Mixture Identifiability
by: Mazaheri, Bijan, et al.
Published: (2024)
by: Mazaheri, Bijan, 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)
Causal Mediation Analysis with Multiple Mediators: A Simulation Approach
by: Zhou, Jesse, et al.
Published: (2025)
by: Zhou, Jesse, et al.
Published: (2025)
A Causal Inference Framework for Data Rich Environments
by: Abadie, Alberto, et al.
Published: (2025)
by: Abadie, Alberto, et al.
Published: (2025)
Doubly Robust Inference in Causal Latent Factor Models
by: Abadie, Alberto, et al.
Published: (2024)
by: Abadie, Alberto, et al.
Published: (2024)
Estimation and Inference for Causal Functions with Multiway Clustered Data
by: Liu, Nan, et al.
Published: (2024)
by: Liu, Nan, et al.
Published: (2024)
Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions
by: Agarwal, Abhineet, et al.
Published: (2023)
by: Agarwal, Abhineet, et al.
Published: (2023)
Identifying Elasticities in Autocorrelated Time Series Using Causal Graphs
by: Tiedemann, Silvana, et al.
Published: (2024)
by: Tiedemann, Silvana, 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)
A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
by: Parikh, Harsh, et al.
Published: (2025)
by: Parikh, Harsh, et al.
Published: (2025)
Long-term Causal Inference Under Persistent Confounding via Data Combination
by: Imbens, Guido, et al.
Published: (2022)
by: Imbens, Guido, et al.
Published: (2022)
Cross-Validated Causal Inference: a Modern Method to Combine Experimental and Observational Data
by: Yang, Xuelin, et al.
Published: (2025)
by: Yang, Xuelin, et al.
Published: (2025)
Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score-Based Estimators
by: Klaassen, Sven, et al.
Published: (2025)
by: Klaassen, Sven, et al.
Published: (2025)
Adaptive Principal Component Regression with Applications to Panel Data
by: Agarwal, Anish, et al.
Published: (2023)
by: Agarwal, Anish, et al.
Published: (2023)
Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal
by: Athey, Susan, et al.
Published: (2023)
by: Athey, Susan, et al.
Published: (2023)
Adaptive, Rate-Optimal Hypothesis Testing in Nonparametric IV Models
by: Breunig, Christoph, et al.
Published: (2020)
by: Breunig, Christoph, et al.
Published: (2020)
Adaptive Estimation and Uniform Confidence Bands for Nonparametric Structural Functions and Elasticities
by: Chen, Xiaohong, et al.
Published: (2021)
by: Chen, Xiaohong, et al.
Published: (2021)
Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choices
by: Kato, Masahiro, et al.
Published: (2024)
by: Kato, Masahiro, et al.
Published: (2024)
Auditing LLMs for Algorithmic Fairness in Casenote-Augmented Tabular Prediction
by: Lee, Xiao Qi, et al.
Published: (2026)
by: Lee, Xiao Qi, et al.
Published: (2026)
Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits
by: Zhan, Ruohan, et al.
Published: (2021)
by: Zhan, Ruohan, et al.
Published: (2021)
Adaptive Student's t-distribution with method of moments moving estimator for nonstationary time series
by: Duda, Jarek
Published: (2023)
by: Duda, Jarek
Published: (2023)
Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series
by: Duda, Jarek
Published: (2025)
by: Duda, Jarek
Published: (2025)
Mining Causality: AI-Assisted Search for Instrumental Variables
by: Han, Sukjin
Published: (2024)
by: Han, Sukjin
Published: (2024)
A note on simulation methods for the Dirichlet-Laplace prior
by: Gruber, Luis, et al.
Published: (2025)
by: Gruber, Luis, et al.
Published: (2025)
Bias-Reduced Estimation of Finite Mixtures: An Application to Latent Group Structures in Panel Data
by: Langevin, Raphaël
Published: (2026)
by: Langevin, Raphaël
Published: (2026)
Robustly estimating heterogeneity in factorial data using Rashomon Partitions
by: Venkateswaran, Aparajithan, et al.
Published: (2024)
by: Venkateswaran, Aparajithan, 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)
Distributed Causality in the SDG Network: Evidence from Panel VAR and Conditional Independence Analysis
by: Fahim, Md Muhtasim Munif, et al.
Published: (2026)
by: Fahim, Md Muhtasim Munif, et al.
Published: (2026)
Deep Learning With DAGs
by: Balgi, Sourabh, et al.
Published: (2024)
by: Balgi, Sourabh, et al.
Published: (2024)
Multi-Band Variable-Lag Granger Causality: A Unified Framework for Causal Time Series Inference across Frequencies
by: Sookkongwaree, Chakattrai, et al.
Published: (2025)
by: Sookkongwaree, Chakattrai, et al.
Published: (2025)
Causal-Policy Forest for End-to-End Policy Learning
by: Kato, Masahiro
Published: (2025)
by: Kato, Masahiro
Published: (2025)
Similar Items
-
Inference for Batched Adaptive Experiments
by: Kemper, Jan, et al.
Published: (2025) -
Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding
by: Rambachan, Ashesh, et al.
Published: (2022) -
Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models
by: Zhou, Guanhao, et al.
Published: (2025) -
A Large-Scale Empirical Comparison of Meta-Learners and Causal Forests for Heterogeneous Treatment Effect Estimation in Marketing Uplift Modeling
by: Singh, Aman
Published: (2026) -
Re-examining Granger Causality with Causal Bayesian Networks and Reichenbachs Principles
by: Adedayo, S. A.
Published: (2025)