Counterfactual Prediction

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3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Attenuated in-context identification in time-series foundation models: diagnosis under counterfactual inputs and repair by synthetic forced-system fine-tuning
Oct 6, 2026Hong-In WonNonlinear System Identification Counterfactual Prediction
Manufacturing AI Research Center, Korea Institute of Industrial Technology, Incheon, Republic of Korea · HYU-KITECH Joint Department, Graduate School, Hanyang University, Seoul, Republic of Korea
Conservation Buys Stability and Factoring Buys Counterfactuals in Physical World Models
Sep 17, 2026Yufeng Wang, Parivesh Priye, Lu Wei +1Neural Surrogate Modeling Dynamical Systems
Stony Brook University · Georgia Institute of Technology · Westlake University
Repurposing Deep Limit Order Book Forecasting for Scenario-Conditioned Market Impact Modeling
Sep 15, 2026Eljas Linna, Kestutis Baltakys, Derrick Manoharan +2Financial Forecasting Counterfactual Prediction
Data Science Research Center, Tampere University, Finland
Conformal Individual Treatment Effect Estimation under Networked Interference
Sep 14, 2026Matteo Zecchin, Osvaldo SimeoneWeighted Conformal Prediction Causal Effect Estimation
Communication Systems Department, EURECOM, Sophia Antipolis, France · Institute for Intelligent Networked Systems (INSI), Northeastern University London, London, UK
Learning Under Treatment-Induced Label Indeterminacy with Expert Annotations of Counterfactual Outcomes: A Case Study in Neurological Prognostication
Aug 12, 2026Xiaobin Shen, Chloe Y. H. Huang, Jonathan Elmer +1Counterfactual Evaluation Clinical Outcome Prediction
Heinz College of Information Systems and Public Policy, Carnegie Mellon University · Department of Emergency Medicine, University of Pittsburgh
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
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
Counterfactual Analysis via Large Language Models
Aug 5, 2026Zonghao YangFinancial Services Counterfactual Reasoning in Language Models
Stevens Institute of Technology
Paired Recipient-based Evaluation of Survival Prediction for Deceased Donor Kidney Transplants
Aug 4, 2026Misaki Matsuura, Mohammadreza Nemati, Dulat Bekbolsynov +2Healthcare Survival Analysis
Department of Computer and Data Sciences Case Western Reserve University Cleveland, OH 44106, USA · Department of Medical Microbiology and Immunology University of Toledo Toledo, OH 43614, USA
ForecastBench-Sim: A Simulated-World Forecasting Benchmark
Jun 17, 2026Jaeho Lee, Nick Merrill, Ezra KargerForecasting Benchmarks Probabilistic Forecasting
1Forecasting Research Institute · 2UC Berkeley
Knowledge-Based Zero-Replay Debugging of Multi-Agent LLM Traces
Jun 11, 2026Dong Ho Kang, Hyeonjeong Cha, Daein WeonMulti-Agent LLM Systems Counterfactual Prediction
aUstechlab, Republic of Korea · bEwha Womans University, Seoul, Republic of Korea · cKookmin University, Seoul, Republic of Korea
Querying Counterfactuals on Tissue Graphs with Supervised Disentanglement
Jun 7, 2026Abdul Moeed, Stefan Schrod, Martin Rohbeck +4Spatial Transcriptomics Disentangled Representation Learning
Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany · Helmholtz Information & Data Science School for Health, Germany · Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany +4
Causal Longitudinal Prior-Fitted Networks for Counterfactual Outcome Prediction
Jun 4, 2026Amirhossein Zare, Amirhessam Zare, Herlock Rahimi +2Structural Causal Models Counterfactual Prediction
School of Medicine, Tehran University of Medical Sciences, Tehran, Iran. · Department of Electrical and Computer Engineering, Yale University, New Haven, CT, USA. · School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran. +2
Benchmarking Counterfactual Prediction in Epidemic Time Series with Time-Varying Interventions
Jun 4, 2026Wenhao Mu, Facundo Yan, Anik Mumssen +2Forecasting Benchmarks Counterfactual Prediction
University of Michigan · Computer Science and Engineering · Ann Arbor, Michigan, USA +1
See Better, Foresee Better, Act Wiser: Physically Grounded Proactive Modeling and Decision Making
Jun 2, 2026Honghui Zhang, Anna Min, Chenmeinian Guo +9Counterfactual Prediction Video Reasoning
Sichuan University · New York University · University of Alberta +6
SL-BiLEM: Structured Learnable Behavior-in-the-Loop Epidemic Modeling for Forecasting and Policy Evaluation
May 26, 2026Haochun Wang, Sendong Zhao, Jingbo Wang +3Epidemic Forecasting Distribution Shift
Faculty of Computing, Harbin Institute of Technology, Harbin, China
From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction
May 16, 2026Pujun Feng, Xiaoyu Guo, Seyed Ehsan Saffari +10Causal Effect Estimation Off-Policy Evaluation
Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR 999078, China. · Medin.ai, Beijing 100871, China. · School of Software & Microelectronics, Peking University, Beijing 100871, China. +8
What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditions
May 14, 2026Shuqi Gu, Yongxiang Zhao, Baoyu Jing +1Counterfactual Evaluation Time Series Forecasting
School of Information Science and Technology, ShanghaiTech University, Shanghai, China · University of Illinois at Urbana-Champaign, Illinois, United States.
Correcting heterogeneous diagnostic bias when developing clinical prediction models using causal hidden Markov models
May 7, 2026Jose Benitez-Aurioles, Ricardo Silva, Brian McMillan +1Counterfactual Prediction Model Calibration
Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, United Kingdom · Department of Statistical Science, UCL, London, United Kingdom · Division of Population Health, Health Services Research & Primary Care, University of Manchester, Manchester, United Kingdom
Resolving the bias-precision paradox with stochastic causal representation learning for personalized medicine
May 7, 2026Peisong Zhang, Manqiang Peng, Yuxuan Wu +21Causal Effect Estimation Causal Representation Learning
School of Medicine, NUS, . · College of Design and Engineering, NUS, . · Aier Academy of Ophthalmology, Central South University. +14
Counterfactual identifiability beyond global monotonicity: non-monotone triangular structural causal models
May 6, 2026Pengcheng Tan, Jiang Chen, Dehui DuStructural Causal Models Causal Counterfactual Generation
Shanghai Key Laboratory of Trustworthy Computing, East China Normal University
From Prediction to Practice: A Task-Aware Evaluation Framework for Blood Glucose Forecasting
May 1, 2026Alireza Namazi, Heman ShakeriHealthcare Clinical Decision Support
Department of Computer Science University of Virginia Charlottesville, VA, USA · School of Data Science University of Virginia Charlottesville, VA, USA
Machine learning models for estimating counterfactuals in a single-arm inflammatory bowel disease study
Apr 25, 2026Dan Liu, Fida K. Dankar, Jennifer C. deBruyn +4Causal Effect Estimation Clinical Outcome Prediction
Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada · School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada · Alberta Children’s Hospital, Calgary, Alberta, Canada +3
A Generalized Synthetic Control Method for Baseline Estimation in Demand Response Services
Apr 20, 2026Jonas Sievers, Mardavij RoozbehaniCounterfactual Prediction Demand Response
Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany · Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Boston, MA 02139, USA
Capture Timing-Attention of Events in Clinical Time Series
Feb 11, 2026Jia Li, Yu Hou, Rui ZhangEvent Time Prediction Healthcare
Department of Surgery; Department of Computer Science, U of M Minneapolis, MN, USA

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Attenuated in-context identification in time-series foundation models: diagnosis under counterfactual inputs and repair by synthetic forced-system fine-tuning

Hong-In Won

Manufacturing AI Research Center, Korea Institute of Industrial Technology, Incheon, Republic of Korea · HYU-KITECH Joint Department, Graduate School, Hanyang University, Seoul, Republic of Korea

Nonlinear System Identification Counterfactual Prediction System Identification Time-Series Foundation Models

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Covariate-aware time-series foundation models (TSFMs) promise training-free what-if answers for instrumented plants: the change in output that a different future input would cause. We test this on forced engineering systems with exact counterfactuals, comparing Chronos-2, TimesFM-2.5 and TabPFN-TS with classical system identification fitted to the same context. Through their default covariate interfaces, TimesFM-2.5 and TabPFN-TS are memoryless: the predicted effect of an input change is a same-time function of that change (

R^2 = 1.000

for TimesFM-2.5). Chronos-2 identifies dynamics in context but attenuates them. Its predicted effect is 0.33-0.80 of the true effect, its recovered impulse response has the wrong shape, and its error on a one-degree-of-freedom oscillator levels off at 0.57 with 8192 context samples, where ARX fitted to 256 samples reaches 0.02. Context dither at inference lowers the what-if error on all six synthetic classes without training. A 26-minute fine-tune on synthetic forced systems restores the response magnitude (sensitivity 0.83-0.96) and outperforms structure-agnostic identification on Wiener-Hammerstein and a held-out friction class. A specialised in-context identifier trained on the same data comes close, so the forced-system data carry most of the gain. On three of four measured plants classical identification remains clearly better, and the fine-tuned model loses part of its univariate forecasting skill. Paired counterfactual inputs, together with shuffled future inputs on measured records, test two properties: whether the covariate interface can represent dynamics and whether the pretraining prior covers the plant's time scale. Only the counterfactual pairs expose the attenuation.

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