Latent Confounders

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Period ending 2026-09-21

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Period ending 2026-09-07

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Latest in Latent Confounders

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Provable Guarantees and Efficient Learning of Structural Equation Models with Latent Confounders
Sep 16, 2026Weijian Yu, Jean HonorioStructural Equation Models Latent Confounders
When Correlations Mislead: Confounder-Aware Multi-View Urban Region Representation Learning
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It's the Problem, Not the Path: Budget and Difficulty Confounds in LLM Reasoning Trajectories
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Evaluation Resolution Confounds Learning-Rule Comparisons in Model-Brain RSA of Early Visual Cortex
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Every Token Counts: Exact Likert-Scale Distributions for Measuring LLM Attitudes and Biases
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When Is a Steerable Concept Representation Real? Measurement Confounds in a Cross-Family Audit of Neuroscience Parallels in LLMs
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Aug 6, 2026Wei-Hsiang Chen, Pin-Hsuan Yu, Chen-Hsuan Fang +1Adversarial Corruption Evaluation Metrics
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Aug 5, 2026Yan Zhang, Yinan Wu, Haoran Duan +1Vision-Language-Action Models Counterfactual Reasoning
Spatiotemporal Proximal Causal Inference under Hidden Confounding and Interference
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Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations
Jul 29, 2026Seunghun Yu, Meiyi Zhu, Petar Popovski +2Change-Point Detection Root Cause Analysis
The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text
Jul 28, 2026Marie Neubrander, Graham Tierney, Alexander VolfovskyLatent Confounders Causal Inferences
Every Client Is an Environment: Federated De-confounding for Spatio-Temporal Forecasting
Jul 27, 2026Qingxiang Liu, Anqi Liang, Heng Wang +1Spatio-Temporal Forecasting Latent Confounders
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Agentic Autoresearch for CT Reconstruction
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Probabilistic Residual Learning for Online Recommendations
Jul 23, 2026Wenyuan Wang, Yusong Zhao, Zihao Xu +11Residual Learning Learning-Augmented Algorithms
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Jul 17, 2026Zhaoyang Jiang, Zhizhong Fu, Zicheng Li +5Sufficient Conditions Latent Confounders
Causal Inference for Sequential Settings under Interference and Latent Confounding
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The Spectral Structure of Latent Treatment Effects
Jul 12, 2026Hamza Virk, Bijan Mazaheri, Yihren WuHeterogeneous Treatment Effects Latent Confounders
Two Confounds in Cross-Model Value Comparison: Response Determinism and the Access Harness
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Jul 10, 2026Debargha Ghosh, Silja Renooij, Anna V. KononovaCausal Foundation Models Latent Confounders
Spectrally Deconfounded Gradient Boosting
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Entanglement as a Structural Complexity Axis: A PAC-Bayesian View of Generalization in Quantum Policies and Value Functions
Jul 7, 2026Jian Xu, Delu Zeng, John Paisley +1Entanglement Entropy-Regularized Equilibrium Selection
Learning Sparsest Linear Causal DAGs with Latent Confounders via Higher-Order Cumulants
Jul 7, 2026Ming Cai, Hisayuki HaraLatent Confounders Directed Acyclic Graph
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Jul 5, 2026Zhongyi Que, Shin Matsushima, Kenji YamanishiCausal Foundation Models Latent Confounders
MemDelta: Controlled Baselines and Hidden Confounds in Agent Memory Evaluation
Jun 29, 2026Kuan WangAgent Memory Single-Agent Baselines
Cross-Head Attention Uplift Network with Inverse Propensity Score under Unobserved Confounding
Jun 25, 2026Haoran Zhang, Chuanpu Li, Yuxin Fu +4Heterogeneous Treatment Effects Propensity
Beyond Simpson's Paradox: A Cascade of Confounders in AI Agent Pull-Request Co-Authorship
Jun 21, 2026Haoran Yu, Xiaochong Jiang, Lifei Liu +3Copilot Artificial Intelligence Agents
Signed Evidence Flow: Conflict-Aware and Stability-Calibrated Data Analysis
Jun 20, 2026Jeffery Opoku, David BanaheneConflict Latent Confounders
Causal Gaussian Processes for Robust Treatment Effect Evaluation with Unobserved Confounding
Jun 20, 2026Junzhe Zhang, Jingyuan Chen, Elias BareinboimCausal Inferences Latent Confounders
Causal Variational Deep Embedding: A Family of Interventional Generators for Confounded Images
Jun 19, 2026Jingyuan Chen, Kangrui Ruan, Junzhe ZhangVariational Autoencoder Latent Confounders
Root Cause Analysis with Latent Confounders using Partial Ancestral Graphs
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Identifying Structural Biases from Causal Mechanism Shifts
Jun 17, 2026Praharsh Nanavati, Jilles Vreeken, David KaltenpothCausal Foundation Models Latent Confounders
Shrinkage priors for Bayesian Substitute Confounders
Jun 16, 2026Yordan P. Raykov, Hengrui Luo, Justin D. Strait +1Latent Confounders Prior Knowledge
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Jun 16, 2026Patrick Blöbaum, Krishnakumar Balasubramanian, Shiva Prasad KasiviswanathanCausal Foundation Models Structural Causal Model
Capacity, Not Format: Rethinking Structured Reasoning Failures
Jun 8, 2026Hengxin FanReasoning Failures Capacity
Shortcut to Nowhere: Demystifying Deep Spurious Regression
Jun 1, 2026Guanrong Xu, Jessica Li, Hao Wang +1Spurious Correlations Latent Confounders
When Are Multimodal Predictions Biologically Supported? A Diagnostic Evaluation Framework
May 29, 2026Dylan Steiner, Gustavo Arango-Argoty, Gerald Sun +1Multimodal Clinical Data Multimodal Classification
Confounder Detection via Treatment Intent: A New Observational Study Design
May 26, 2026Drago Plecko, Patrik Okanovic, Torsten Hoefler +1Latent Confounders Causal Inferences
Robust Personalized Recommendation under Hidden Confounding in MNAR
May 20, 2026Zongyu Li, Wanting Su, Tianyu XiaLatent Confounders Selection Bias
Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions
May 20, 2026Zeyu Liu, Zheng Li, Feng Xie +3Covariate Shift Latent Confounders
The Illusion of Intervention: Your LLM-Simulated Experiment is an Observational Study
May 20, 2026Victoria Lin, Taedong Yun, Maja Matarić +3User Simulation Causal Intervention
Robust Mitigation of Age-Dependent Confounding Effects via Sample-Difficulty Decorrelation
May 19, 2026Nikhil Cherian Kurian, Victor Caquilpan Parra, Abin Shoby +2Medical Image Classification Latent Confounders
From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction
May 16, 2026Pujun Feng, Xiaoyu Guo, Seyed Ehsan Saffari +10Clinical Prediction Clinical Decision Support
The Last Word Often Wins: A Format Confound in Chain-of-Thought Corruption Studies
May 11, 2026Gabriel GarciaChain-of-Thought Reasoning Correctness
Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networks
May 11, 2026Emil Javurek, Dennis Frauen, Marie Brockschmidt +2Model Sensitivity Causal Inferences
ConfoundingSHAP: Quantifying confounding strength in causal inference
May 11, 2026Marie Brockschmidt, Santo M. A. R. Thies, Maresa Schröder +5Latent Confounders Causal Inferences
Debiased Counterfactual Generation via Flow Matching from Observations
May 8, 2026Hugh Dance, Johnny Xi, Peter Orbanz +1Latent Confounders Conditional Distribution
Physical Simulators as Do-Operators: Causal Discovery under Latent Confounders for AI-for-Science
May 8, 2026Tsuyoshi OkitaCausal Foundation Models Latent Confounders
The Frequency Confound in Language-Model Surprisal and Metaphor Novelty
May 7, 2026Omar Momen, Sina ZarrießHigh Bigram Repetition Frequency Surprisal
eXplaining to Learn (eX2L): Regularization Using Contrastive Visual Explanation Pairs for Distribution Shifts
May 7, 2026Paulo Mario P. Medina, Jose Marie Antonio Miñoza, Sebastian C. IbañezExplainability Distribution Shifts
When Prompts Interact: Assessing Prompt Arithmetic for Deconfounding under Distribution Shift
May 4, 2026Zhecheng Sheng, Yongsen Tan, Xiruo Ding +2Distribution Shifts Prompt Tuning
MOCA: A Transformer-based Modular Causal Inference Framework with One-way Cross-attention and Cutting Feedback
Apr 25, 2026Lei Wang, Debashis GhoshCausal Inferences Latent Confounders
Competition-Aware CPC Forecasting with Near-Market Coverage
Mar 13, 2026Sebastian Frey, Edoardo Beccari, Maximilian Kranz +4Click-Through Rate Prediction Covariate Shift
Teacher-Guided Causal Interventions for Image Denoising: Orthogonal Content-Noise Disentanglement in Vision Transformers
Mar 1, 2026Kuai Jiang, Zhaoyan Ding, Guijuan Zhang +2Image Denoising Self-Supervised Vision Transformers
Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference
Oct 9, 2025Ayush Khot, Miruna Oprescu, Maresa Schröder +2Latent Confounders Causal Inferences
Causal Discovery in Mixtures of Populations
Nov 13, 2023Bijan Mazaheri, Spencer Gordon, Yuval Rabani +1Causal Foundation Models Structural Equation Models
Aligning Language Models with Observational Data: Opportunities and Risks from a Causal Perspective
Date pendingErfan LoghmaniLarge Language Model Fine-Tuning Large Language Model Performance

Sep 16, 2026 · cs.LGJ/K move · Enter open · S save

Provable Guarantees and Efficient Learning of Structural Equation Models with Latent Confounders

Weijian Yu, Jean Honorio

Causal discovery aims to recover causal relationships from observed data. In various fields, exploring causal relationships among variables remains an important topic, but this task becomes challenging due to the existence of latent confounders. Ignoring such confounders can lead to false associations and incorrect edge directions. In this paper, we study the linear structural equation model with latent confounders. We propose an algorithm that iteratively identifies terminal (observed) nodes and reconstructs the directed acyclic graph of the observed variables. To do this, we recover the precision matrix of the observed variables as a sparse plus low-rank matrix: a sparse matrix captures the conditional dependencies among observed variables, while a low-rank matrix captures the combined influence of a few latent confounders. We establish that for

p

observed variables,

r

latent confounders and

s

edges, our procedure correctly identifies the directed causal relationship among observed variables, for

n \gtrsim \max\{s\log p,\ r p\}

samples. Experimental results validate our theoretical contributions.

Structural Equation Models Latent Confounders Causal Reasoning Latent Variable Provable Guarantees

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