Cover Image

PCS Workflow for Veridical Data Science in the Age of AI

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Rewolinski, Zachary T., Yu, Bin
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Veridical Data Science for Medical Foundation Models
by: Alaa, Ahmed, et al.
Published: (2024)

Sanity Checks for Agentic Data Science
by: Rewolinski, Zachary T., et al.
Published: (2026)

Local MDI+: Local Feature Importances for Tree-Based Models
by: Liang, Zhongyuan, et al.
Published: (2025)

When Should an AI Workflow Release? Always-Valid Inference for Black-Box Generate-Verify Systems
by: Cho, Young Hyun, et al.
Published: (2026)

Operationalizing Longitudinal Causal Discovery Under Real-World Workflow Constraints
by: Okuda, Tadahisa, et al.
Published: (2026)

A Framework for Nonstationary Gaussian Processes with Neural Network Parameters
by: James, Zachary, et al.
Published: (2025)

VDSAgents: A PCS-Guided Multi-Agent System for Veridical Data Science Automation
by: Jiang, Yunxuan, et al.
Published: (2025)

Discriminative classification with generative features: bridging Naive Bayes and logistic regression
by: Terner, Zachary, et al.
Published: (2025)

Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretability
by: Agarwal, Abhineet, et al.
Published: (2023)

Statistical Methods in Generative AI
by: Dobriban, Edgar
Published: (2025)

AI-Powered Bayesian Inference
by: O'Hagan, Sean, et al.
Published: (2025)

Robust Classification of High-Dimensional Data using Data-Adaptive Energy Distance
by: Choudhury, Jyotishka Ray, et al.
Published: (2023)

The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology
by: Gamella, Juan L., et al.
Published: (2024)

An AI-powered Bayesian generative modeling approach for causal inference in observational studies
by: Liu, Qiao, et al.
Published: (2025)

Federated Causal Inference from Observational Data
by: Vo, Thanh Vinh, et al.
Published: (2023)

Comparing Foundation Models using Data Kernels
by: Duderstadt, Brandon, et al.
Published: (2023)

Data-Driven Estimation of Heterogeneous Treatment Effects
by: Tran, Christopher, et al.
Published: (2023)

PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework
by: Agarwal, Abhineet, et al.
Published: (2025)

BGM-IV: an AI-powered Bayesian generative modeling approach for instrumental variable analysis
by: Luo, Guyue, et al.
Published: (2026)

Learning DAGs from Data with Few Root Causes
by: Misiakos, Panagiotis, et al.
Published: (2023)

Review for Handling Missing Data with special missing mechanism
by: Zhou, Youran, et al.
Published: (2024)

Industrializing Prediction-Powered Inference: The GLIDE Library for Reliable GenAI and Agentic Systems Evaluation
by: Martinon, Grégoire, et al.
Published: (2026)

Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities
by: Xu, Yichen
Published: (2026)

Mechanisms for Data Sharing in Collaborative Causal Inference (Extended Version)
by: Filter, Björn, et al.
Published: (2024)

A Generalized Similarity U Test for Multivariate Analysis of Sequencing Data
by: Wei, Changshuai, et al.
Published: (2015)

A General Causal Inference Framework for Cross-Sectional Observational Data
by: Zhao, Yonghe, et al.
Published: (2024)

Data-Augmented Few-Shot Neural Emulator for Computer-Model System Identification
by: Jantre, Sanket, et al.
Published: (2025)

Unsupervised Pairwise Causal Discovery on Heterogeneous Data using Mutual Information Measures
by: Trilla, Alexandre, et al.
Published: (2024)

Teleporter Theory: A General and Simple Approach for Modeling Cross-World Counterfactual Causality
by: Li, Jiangmeng, et al.
Published: (2024)

A Generalized Genetic Random Field Method for the Genetic Association Analysis of Sequencing Data
by: Li, Ming, et al.
Published: (2025)

Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery
by: Shen, ChengAo, et al.
Published: (2024)

RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model
by: Li, Peiwen, et al.
Published: (2024)

A Data-Driven Two-Phase Multi-Split Causal Ensemble Model for Time Series
by: Ma, Zhipeng, et al.
Published: (2024)

TTCD:Transformer Integrated Temporal Causal Discovery from Non-Stationary Time Series Data
by: Faruque, Omar, et al.
Published: (2026)

Augmenting Limited and Biased RCTs through Pseudo-Sample Matching-Based Observational Data Fusion Method
by: Han, Kairong, et al.
Published: (2025)

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data
by: Garg, Anurag, et al.
Published: (2025)

A Two-Stage Feature Selection Approach for Robust Evaluation of Treatment Effects in High-Dimensional Observational Data
by: Islam, Md Saiful, et al.
Published: (2021)

Model-Free Assessment of Simulator Fidelity via Quantile Curves
by: Iyengar, Garud, et al.
Published: (2025)

Combining Priors with Experience: Confidence Calibration Based on Binomial Process Modeling
by: Dong, Jinzong, et al.
Published: (2024)

Effective Causal Discovery under Identifiable Heteroscedastic Noise Model
by: Yin, Naiyu, et al.
Published: (2023)