Causal

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Period ending 2026-09-21

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Causal Reasoning

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Covariate Shift

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Causal Foundation Models

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Causal Inferences

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Causal Graph

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Structural Causal Model

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Heterogeneous Treatment Effects

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Latent Confounders

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Treatment

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Propensity

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Causal Representation Learning

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Causal Intervention

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944 papers

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First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint
May 4, 2026Ziqi Liu, Kiljae Lee, Yuan Zhang +1Estimators Propensity
The Design and Composition of Structural Causal Decision Processes
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The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy
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Weight Clipping for Robust Conformal Inference under Unbounded Covariate Shifts
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Principles and Guidelines for Randomized Controlled Trials in AI Evaluation
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Distributional Causal Mediation via Conditional Generative Modeling
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Self-Normalized Martingales and Uniform Regret Bounds for Linear Regression
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Minimum Specification Perturbation: Robustness as Distance-to-Falsification in Causal Inference
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Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation
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Virtual Speech Therapist: A Clinician-in-the-Loop AI Speech Therapy Agent for Personalized and Supervised Therapy
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May 1, 2026J. Jake Nichol, Michael Weylandt, G. Matthew Fricke +2Causal Foundation Models Causal Reasoning
Embodied Interpretability: Linking Causal Understanding to Generalization in Vision-Language-Action Models
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Caracal: Causal Architecture via Spectral Mixing
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Minimal, Local, Causal Explanations for Jailbreak Success in Large Language Models
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Perturbation Probing: A Two-Pass-per-Prompt Diagnostic for FFN Behavioral Circuits in Aligned LLMs
Apr 30, 2026Hongliang Liu, Tung-Ling Li, Yuhao WuPerturbations Large Language
Bayesian X-Learner: Calibrated Posterior Inference for Heterogeneous Treatment Effects under Heavy-Tailed Outcomes
Apr 30, 2026Eichi UeharaHeterogeneous Treatment Effects Posterior
A Novel Computational Framework for Causal Inference: Tree-Based Discretization with ILP-Based Matching
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Apr 27, 2026Liangru Xiang, Yuxi Ma, Zhihao Cao +2Causal Reasoning Analogy
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Causal Representation Learning from General Environments under Nonparametric Mixing
Apr 26, 2026Ignavier Ng, Shaoan Xie, Xinshuai Dong +2Causal Representation Learning Latent Variable
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Machine learning models for estimating counterfactuals in a single-arm inflammatory bowel disease study
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CASP: Support-Aware Offline Policy Selection for Two-Stage Recommender Systems
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TabSCM: A practical Framework for Generating Realistic Tabular Data
Apr 24, 2026Sven Jacob, Bardh Prenkaj, Weijia Shao +1Synthetic Tabular Data Causal Foundation Models
Dissociating Decodability and Causal Use in Bracket-Sequence Transformers
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Causal Disentanglement-Inspired Degradation Representation Learning for Full-Reference Image Quality Assessment
Apr 23, 2026Zhen Zhang, Jielei Chu, Tian Zhang +5Image Quality Assessment Representation Learning
Unbiased Prevalence Estimation with Multicalibrated LLMs
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HiCrew: Hierarchical Reasoning for Long-Form Video Understanding via Question-Aware Multi-Agent Collaboration
Apr 23, 2026Yuehan Zhu, Jingqi Zhao, Jiawen Zhao +2Agentic Video Generation Multi-Agent Reasoning

May 4, 2026 · cs.AIJ/K move · Enter open · S save

First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint

Ziqi Liu, Kiljae Lee, Yuan Zhang, Weijing Tang

Department of Statistics & Data Science, Carnegie Mellon University. · Department of Statistics, The Ohio State University.

Probabilistic values, including Shapley values and semivalues, provide a model-agnostic framework to attribute the behavior of a black-box model to data points or features, with a wide range of applications including explainable artificial intelligence and data valuation. However, their exact computation requires utility evaluations over exponentially many coalitions, making Monte Carlo approximation essential in modern machine learning applications. Existing estimators are often developed through different representation strategies, including weighted averages, self-normalized weighting, regression adjustment, and weighted least squares. Our key observation is that these seemingly distinct constructions share a common first-order expansion, in which the leading term is determined by the sampling law and a working surrogate function. This first-order representation yields an explicit expression for the leading mean squared error (MSE), which characterizes how the sampling law and the surrogate jointly determine statistical efficiency. Guided by this criterion, we propose an Efficiency-Aware Surrogate-adjusted Estimator (EASE) that directly chooses the sampling law and surrogate to minimize the first-order MSE. We demonstrate that EASE consistently outperforms existing estimators for various probabilistic values.

Estimators Propensity Statistical Inference Monte Carlo Value Black Box

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