Causal Inference

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21 papers in the last four weeks, up 62% on the four weeks before. 0.2% of all new papers.

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Difference-in-Differences on a Censored Rating Scale Can Manufacture an Effect: Evidence from a Pre-Registered LLM-Judge Audit
Aug 27, 2026Shuyi Fan, Boyuan Deng, Mengyu Xu +3Causal Inference LLM Auditing
The University of Chicago · Johns Hopkins University · The Hong Kong Polytechnic University, Hong Kong
Intervention, Not Shared Latents: Blocking Visual Shortcuts in Audio-Video Generation
Aug 14, 2026Jian Xu, Delu Zeng, John PaisleyAudio-Video Generation Causal Inference
RIKEN iTHEMS · RIKEN AIP · South China University of Technology +1
General Probabilities of Causation with Causal Knowledge
Aug 12, 2026Xin Shu, Zhen Lei, Ang LiPartial Identification Causal Identifiability
Department of Computer Science, Florida State University
Causal inference for group-contaminated structured outcomes: observable quotients, lossless reduction and exact randomization inference
Aug 12, 2026Usef Faghihi, Amir SakiCausal Inference
D´epartement de math´ematiques et d’informatique Universit´e du Qu´ebec `a Trois-Rivi`eres Trois-Rivi`eres, Qu´ebec, Canada
How Can Driving World Models Do Counterfactual Prediction?
Aug 12, 2026Jiaru Zhang, Can Cui, Yi Xu +3Counterfactual Evaluation Causal Counterfactual Generation
Purdue University · Bosch Center for Artificial Intelligence
Conditional Independence Tests for Constraint-Based Causal Discovery: A Survey
Aug 11, 2026Pavel Averin, Theodoros Moysiadis, Ioannis KatakisCausal Discovery Causal Inference
Department of Computer Science School of Sciences and Engineering University of Nicosia 2417, Nicosia, Cyprus
Expert-Guided g-computation with Large Language Models for Estimating Causal Effects on Timings: Applications to Hospital Quality Improvement
Aug 11, 2026Patrick Vossler, Jialin Ouyang, F. Richard Guo +5Causal Effect Estimation Structural Causal Models
University of California, San Francisco · University of Michigan, Ann Arbor · University of Washington, Seattle
From Prediction to Incrementality: Causal Optimization for Large-Scale Targeting and Recommendation
Aug 10, 2026Changshuai Wei, John Bencina, Phuc Nguyen +2Causal Effect Estimation Constrained Optimization
LinkedIn · Seattle, USA · Sunnyvale, USA
A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning
Aug 9, 2026Jianhan Zhang, Jitao Wang, John D. Piette +3Causal Counterfactual Generation Algorithmic Fairness
University of Michigan · LSE
Causal State-Space Model for Causal Inference: Estimating Longitudinal Individual Treatment Effects
Aug 8, 2026Abisoye Abidakun, Mingjun Zhong, Georgios LeontidisCausal Effect Estimation Selective SSMs
Department of Computing Science, University of Aberdeen, Aberdeen, UK · UiT The Arctic University of Norway
Conditional Diffusion for Nonparametric Instrumental Variable Quantile Regression
Aug 8, 2026Xingdong Feng, Xinhong Jiang, Yuling Jiao +2Quantile Regression Nonparametric Regression
School of Statistics and Data Science, Institute of Data Science and Statistics, Shanghai University of Finance and Economics, Shanghai 200433, China · School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China · School of Artificial Intelligence, and Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan, 430072, China +1
Adversarial Causal Intervention Falsification
Aug 5, 2026Mojtaba EslamiStructural Causal Models Causal Discovery
University of Calgary
Implementing Causal Perception: Competing SCMs and Situated Fairness
Aug 4, 2026Jose M. ÁlvarezStructural Causal Models Algorithmic Fairness
Santander AI Lab, Madrid, Spain
When Many Answers Are Valid, Voting Fails: Symbolic Verification for Best-of-K Causal Reasoning in LLMs
Aug 4, 2026Omatharv Bharat Vaidya, Connor Thomas Jerzak, Zayne Rea Sprague +2Self-Consistency Decoding Symbolic Reasoning
The University of Texas at Austin · New York University
Causal Inference with Unstructured Outcomes
Aug 4, 2026Kevin Christian Wibisono, Yixin WangCausal Effect Estimation Causal Identifiability
Department of Statistics University of Michigan, Ann Arbor
Robust Counterfactual Policy Optimisation via Nondeterministic Causal Models
Aug 3, 2026Jessica Lally, Milad Kazemi, Nicola Paoletti +2Markov Decision Processes Structural Causal Models
1King’s College London · 2Durham University · University College London
Spatiotemporal Proximal Causal Inference under Hidden Confounding and Interference
Aug 2, 2026Omar Faruque, Pavan Raj Ravi, Jianwu WangCausal Effect Estimation Causal Identifiability
University of Maryland, Baltimore County
Passing Coarse Marginal Checks Can Be Cheap: Persona Mixtures and Imprecise Treatment-Response Estimates in an LLM Persona Panel
Aug 2, 2026Yohei NakajimaPrompt Sensitivity LLM Evaluation
Untapped Capital
Causal Inference with Unstructured Treatments
Aug 1, 2026Kevin Christian Wibisono, Yixin WangCausal Effect Estimation Causal Inference
Department of Statistics University of Michigan, Ann Arbor
A robust association between LLM use and scientific productivity: Assessing stopping-time selection
Jul 31, 2026Keigo Kusumegi, Xinyu Yang, Paul Ginsparg +3AI for Science Causal Effect Estimation
Doubly Robust Functional Representation Learning for Longitudinal Causal Inference with Irregular Histories
Jul 30, 2026Mengfei Ran, Yifeng Shen, Ruijie GuanCausal Representation Learning Causal Inference
Academy of Life and Natural Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, China · Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Orca: Neural Operators for Causal Reasoning in Continuous Time
Jul 30, 2026Gerrit Großmann, David A. Selby, Sebastian J. VollmerStructural Causal Models Causal Counterfactual Generation
German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
Same Evidence, Different Target: Decoding How Diagnostic Evidence Bears on Causal Questions from Language-Model States
Jul 29, 2026Weiyi Kong, Zhuoran LiCausal Reasoning in Language Models Causal Inference
University of Toronto, Toronto, Canada · The University of Hong Kong, Hong Kong
Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation
Jul 29, 2026Jialu Xu, Mengkun Liang, Guannan Liu +2Representation Learning Causal Representation Learning
MIIT Key Laboratory of Data and Decision Intelligence, Beihang University Beijing, China
The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text
Jul 28, 2026Marie Neubrander, Graham Tierney, Alexander VolfovskyCausal Effect Estimation Causal Representation Learning
Department of Statistical Science Duke University Durham, NC, 27705
Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series
Jul 27, 2026Mohammad FesangharyTime-Series Causal Discovery Causal Discovery
Bloomberg LP, New York, NY, USA
proxymate: Diagnosis and Adjustment of Proxy Estimates for Reliable Inference
Jul 27, 2026Alexandra N. M. Darmon, Deeksha Sinha, Steve Wilkins-Reeves +1Causal Inference Uncertainty Calibration
Meta
Every Client Is an Environment: Federated De-confounding for Spatio-Temporal Forecasting
Jul 27, 2026Qingxiang Liu, Anqi Liang, Heng Wang +1Domain Generalization Causal Inference
The Hong Kong University of Science and Technology (Guangzhou) · Shanghai Jiao Tong University · Harbin Engineering University
CausalSmith: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference
Jul 24, 2026Jiyuan Tan, Vasilis SyrgkanisAI Agents for Scientific Discovery Automated Theorem Proving
Interventional Score Geometry for Causal Inference
Jul 24, 2026Mojtaba EslamiInformation Geometry Structural Causal Models
Post-doctoral Researcher, University of Calgary.

Aug 27, 2026, cs.CLJ/K move · Enter open · S save

Difference-in-Differences on a Censored Rating Scale Can Manufacture an Effect: Evidence from a Pre-Registered LLM-Judge Audit

Shuyi Fan, Boyuan Deng, Mengyu Xu, Xinhong Xie+2

The University of Chicago · Johns Hopkins University · The Hong Kong Polytechnic University, Hong Kong

Causal Inference LLM Auditing LLM-as-a-Judge

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LLM-judge audits assess bias by comparing ratings across matched conditions. Difference-in-differences designs compare two candidate responses within each item and then compare that contrast across a manipulated attribute. We show in closed form that this endpoint need not identify differential preference on the latent scale when ratings are bounded. A severity shift common to both responses produces an observed interaction whenever the scale bounds attenuate it unequally. We examine this problem in a pre-registered audit comprising 990 calls to a frozen pedagogy judge. The registered primary analysis did not detect an effect of the stated learner profile on scaffolding preference. The only nominally significant secondary interaction concerned productive struggle (

+0.378

points;

p = 0.002

). A post-hoc construction with zero differential preference reproduced 79 to 85% of this interaction using the observed severity shift and the scale floor alone. The observed interaction therefore does not identify differential preference on the latent scale.

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