Cover Image

Honesty in Causal Forests: When It Helps and When It Hurts

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hou, Yanfang, Fernández-Loría, Carlos
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Causal Post-Processing of Predictive Models
by: Fernández-Loría, Carlos, et al.
Published: (2024)

Synthetic Augmentation in Imbalanced Learning: When It Helps, When It Hurts, and How Much to Add
by: Ma, Zhengchi, et al.
Published: (2026)

The Amenability Framework: Rethinking Causal Ordering Without Estimating Causal Effects
by: Fernández-Loría, Carlos, et al.
Published: (2025)

Causal Ordering Without Effect Estimation: A Framework for Using Proxies in Treatment Prioritization
by: Fernández-Loría, Carlos, et al.
Published: (2022)

When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation
by: Liu, Yi
Published: (2026)

The Pitfalls of Memorization: When Memorization Hurts Generalization
by: Bayat, Reza, et al.
Published: (2024)

Causal Inference Isn't Special: Why It's Just Another Prediction Problem
by: Fernández-Loría, Carlos
Published: (2025)

Not All Queries Need Rewriting: When Prompt-Only LLM Refinement Helps and Hurts Dense Retrieval
by: Kotte, Varun
Published: (2026)

Graph Representational Learning: When Does More Expressivity Hurt Generalization?
by: Maskey, Sohir, et al.
Published: (2025)

When More Experts Hurt: Underfitting in Multi-Expert Learning to Defer
by: Liu, Shuqi, et al.
Published: (2026)

When Learning Hurts: Fixed-Pole RNN for Real-Time Online Training
by: Morgan, Alexander, et al.
Published: (2026)

When Correct Demonstrations Hurt: Rethinking the Role of Exemplars in In-Context Learning
by: Qiu, Chenghao, et al.
Published: (2026)

LoRA and Privacy: When Random Projections Help (and When They Don't)
by: Hu, Yaxi, et al.
Published: (2026)

When do Random Forests work?
by: Revelas, C., et al.
Published: (2025)

When Shared Knowledge Hurts: Spectral Over-Accumulation in Model Merging
by: Li, Yayuan, et al.
Published: (2026)

Unmasking On-Policy Distillation: Where It Helps, Where It Hurts, and Why
by: Armandpour, Mohammadreza, et al.
Published: (2026)

When Hard Negatives Hurt: Bridging the Generative-Discriminative Gap in Hard Negative Synthesis for Retrieval
by: Zhang, Zhicheng, et al.
Published: (2026)

When does Chain-of-Thought Help: A Markovian Perspective
by: Wang, Zihan, et al.
Published: (2026)

How do Large Language Models Navigate Conflicts between Honesty and Helpfulness?
by: Liu, Ryan, et al.
Published: (2024)

When Fusion Helps and When It Breaks: View-Aligned Robustness in Same-Source Financial Imaging
by: Ma, Rui
Published: (2026)

When More Parameters Hurt: Foundation Model Priors Amplify Worst-Client Disparity Under Extreme Federated Heterogeneity
by: Naseer, Kiran, et al.
Published: (2026)

When Do "More Contexts" Help with Sarcasm Recognition?
by: Nimase, Ojas, et al.
Published: (2024)

When Reasoning Hurts: Source-Aware Evaluation of Frontier LLMs for Clinical SOAP Note Generation
by: Faisal, Faizan
Published: (2026)

Why Self-Training Helps and Hurts: Denoising vs. Signal Forgetting
by: Wu, Mingqi, et al.
Published: (2026)

When Dimensionality Hurts: The Role of LLM Embedding Compression for Noisy Regression Tasks
by: Drinkall, Felix, et al.
Published: (2025)

R2V Agent: Teaching SLMs When to Ask for Help
by: Hemadri, Raghu Vamshi, et al.
Published: (2026)

When and How Does In-Distribution Label Help Out-of-Distribution Detection?
by: Du, Xuefeng, et al.
Published: (2024)

When More Data Doesn't Help: Limits of Adaptation in Multitask Learning
by: Hanneke, Steve, et al.
Published: (2026)

What Helps---and What Hurts: Bidirectional Explanations for Vision Transformers
by: Su, Qin, et al.
Published: (2026)

When Selection Meets Intervention: Additional Complexities in Causal Discovery
by: Dai, Haoyue, et al.
Published: (2025)

When Do LLMs Help With Node Classification? A Comprehensive Analysis
by: Wu, Xixi, et al.
Published: (2025)

Training LLMs for Honesty via Confessions
by: Joglekar, Manas, et al.
Published: (2025)

When De-noising Hurts: A Systematic Study of Speech Enhancement Effects on Modern Medical ASR Systems
by: Chondhekar, Sujal, et al.
Published: (2025)

When Reasoning Models Hurt Behavioral Simulation: A Solver-Sampler Mismatch in Multi-Agent LLM Negotiation
by: Andric, Sandro
Published: (2026)

Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging
by: Yang, Jinluan, et al.
Published: (2025)

When Structure Doesn't Help: LLMs Do Not Read Text-Attributed Graphs as Effectively as We Expected
by: Xu, Haotian, et al.
Published: (2025)

When Does Trimming Help Conformal Prediction? A Retained-Law Diagnostic under Calibration Contamination
by: Wang, Congye
Published: (2026)

When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions
by: Li, Zhening, et al.
Published: (2024)

When Generative Augmentation Hurts: A Benchmark Study of GAN and Diffusion Models for Bias Correction in AI Classification Systems
by: Gupta, Shesh Narayan, et al.
Published: (2026)

Bayesian Meta-Learning with Expert Feedback for Task-Shift Adaptation through Causal Embeddings
by: Mäkinen, Lotta, et al.
Published: (2026)