Image de couverture de livre

LLM Personas as a Substitute for Field Experiments in Method Benchmarking

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Kang, Enoch Hyunwook
Format:	Preprint
Publié:	2025
Sujets:	Artificial Intelligence Machine Learning Econometrics
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

A Lecture Note on Offline RL and IRL, Part II: Foundations of Inverse Reinforcement Learning and Dynamic Discrete Choice Models
par: Kang, Enoch Hyunwook
Publié: (2026)

An Empirical Risk Minimization Approach for Offline Inverse RL and Dynamic Discrete Choice Model
par: Kang, Enoch H., et autres
Publié: (2025)

Personalized Alignment Revisited: The Necessity and Sufficiency of User Diversity
par: Kang, Enoch Hyunwook
Publié: (2026)

Simulation-Based Benchmarking of Reinforcement Learning Agents for Personalized Retail Promotions
par: Xia, Yu, et autres
Publié: (2024)

Demystifying the unreasonable effectiveness of online alignment methods
par: Kang, Enoch Hyunwook
Publié: (2026)

Adaptive Experimental Design for Policy Learning
par: Kato, Masahiro, et autres
Publié: (2024)

A Double Machine Learning Approach to Combining Experimental and Observational Data
par: Parikh, Harsh, et autres
Publié: (2023)

Semiparametric Preference Optimization: Your Language Model is Secretly a Single-Index Model
par: Kallus, Nathan
Publié: (2025)

Foundation Priors
par: Misra, Sanjog
Publié: (2025)

A Hybrid Framework for Reinsurance Optimization: Integrating Generative Models and Reinforcement Learning
par: Dong, Stella C.
Publié: (2025)

DeXposure-FM: A Time-series, Graph Foundation Model for Credit Exposures and Stability on Decentralized Financial Networks
par: Shu, Aijie, et autres
Publié: (2026)

Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networks
par: Patil, Gandharv, et autres
Publié: (2026)

Auditing and Fixing Economic Validity in Tabular Foundation Models for Discrete Choice
par: Wang, Yingshuo, et autres
Publié: (2026)

Management Decisions in Manufacturing using Causal Machine Learning -- To Rework, or not to Rework?
par: Schwarz, Philipp, et autres
Publié: (2024)

GDP nowcasting with artificial neural networks: How much does long-term memory matter?
par: Németh, Kristóf, et autres
Publié: (2023)

Generating density nowcasts for U.S. GDP growth with deep learning: Bayes by Backprop and Monte Carlo dropout
par: Németh, Kristóf, et autres
Publié: (2024)

How Well Do LLMs Predict Human Behavior? A Measure of their Pretrained Knowledge
par: Gao, Wayne, et autres
Publié: (2026)

Structural Estimation of Markov Decision Processes in High-Dimensional State Space with Finite-Time Guarantees
par: Zeng, Siliang, et autres
Publié: (2022)

Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators
par: Huang, Yiyan, et autres
Publié: (2024)

Reinforcement Learning for Monetary Policy Under Macroeconomic Uncertainty: Analyzing Tabular and Function Approximation Methods
par: Wang, Tony, et autres
Publié: (2025)

Selective Reviews of Bandit Problems in AI via a Statistical View
par: Zhou, Pengjie, et autres
Publié: (2024)

Global Ease of Living Index: a machine learning framework for longitudinal analysis of major economies
par: Panat, Tanay, et autres
Publié: (2025)

Multi-Band Variable-Lag Granger Causality: A Unified Framework for Causal Time Series Inference across Frequencies
par: Sookkongwaree, Chakattrai, et autres
Publié: (2025)

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification
par: Kato, Masahiro, et autres
Publié: (2025)

From What Ifs to Insights: Counterfactuals in Causal Inference vs. Explainable AI
par: Shmueli, Galit, et autres
Publié: (2025)

Towards Generalizing Inferences from Trials to Target Populations
par: Huang, Melody Y, et autres
Publié: (2024)

Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
par: Jin, Jikai, et autres
Publié: (2023)

Statistical Tests for Replacing Human Decision Makers with Algorithms
par: Feng, Kai, et autres
Publié: (2023)

$ρ$-GNF: A Copula-based Sensitivity Analysis to Unobserved Confounding Using Normalizing Flows
par: Balgi, Sourabh, et autres
Publié: (2022)

Econometric vs. Causal Structure-Learning for Time-Series Policy Decisions: Evidence from the UK COVID-19 Policies
par: Petrungaro, Bruno, et autres
Publié: (2026)

Generalized Neyman Allocation for Locally Minimax Optimal Best-Arm Identification
par: Kato, Masahiro
Publié: (2024)

Differentially Private Two-Stage Gradient Descent for Instrumental Variable Regression
par: Liang, Haodong, et autres
Publié: (2025)

Causality Elicitation from Large Language Models
par: Kameyama, Takashi, et autres
Publié: (2026)

Transformers Handle Endogeneity in In-Context Linear Regression
par: Liang, Haodong, et autres
Publié: (2024)

What Is the Alignment Tax?
par: Young, Robin
Publié: (2026)

A Job I Like or a Job I Can Get: Designing Job Recommender Systems Using Field Experiments
par: Bied, Guillaume, et autres
Publié: (2026)

Non-linear Phillips Curve for India: Evidence from Explainable Machine Learning
par: Sengupta, Shovon, et autres
Publié: (2025)

Unified Causality Analysis Based on the Degrees of Freedom
par: Telcs, András, et autres
Publié: (2024)

Enhancing Preference-based Linear Bandits via Human Response Time
par: Li, Shen, et autres
Publié: (2024)

Calibeating Prediction-Powered Inference
par: van der Laan, Lars, et autres
Publié: (2026)