{"total":49,"rows":[{"id":"2511.08667","title":"TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models","created":"2025-11-11","cited_by_count":132,"area":null},{"id":"2506.07918","title":"CausalPFN: Amortized Causal Effect Estimation via In-Context Learning","created":"2025-06-09","cited_by_count":49,"area":null},{"id":"2506.10914","title":"Foundation Models for Causal Inference via Prior-Data Fitted Networks","created":"2025-06-12","cited_by_count":32,"area":null},{"id":"2605.13986","title":"TabPFN-3: Technical Report","created":"2026-05-13","cited_by_count":17,"area":null},{"id":"2603.03805","title":"Relational In-Context Learning via Synthetic Pre-training with Structural Prior","created":"2026-03-04","cited_by_count":14,"area":null},{"id":"2602.03018","title":"From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detection","created":"2026-02-03","cited_by_count":9,"area":null},{"id":"2602.14972","title":"Use What You Know: Causal Foundation Models with Partial Graphs","created":"2026-02-16","cited_by_count":7,"area":null},{"id":"2603.11090","title":"Interventional Time Series Priors for Causal Foundation Models","created":"2026-03-11","cited_by_count":7,"area":null},{"id":"2603.29475","title":"Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks","created":"2026-03-31","cited_by_count":6,"area":null},{"id":"2511.07236","title":"Does TabPFN Understand Causal Structures?","created":"2025-11-10","cited_by_count":5,"area":null},{"id":"2605.15133","title":"Causal Foundation Models with Continuous Treatments","created":"2026-05-14","cited_by_count":4,"area":null},{"id":"2605.28880","title":"Towards Continuous-time Causal Foundation Models","created":"2026-05-26","cited_by_count":4,"area":null},{"id":"2601.21092","title":"MapPFN: Learning Causal Perturbation Maps in Context","created":"2026-01-28","cited_by_count":3,"area":null},{"id":"2605.10590","title":"Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networks","created":"2026-05-11","cited_by_count":3,"area":null},{"id":"2605.12924","title":"IV-ICL: Bounding Causal Effects with Instrumental Variables via In-Context Learning","created":"2026-05-13","cited_by_count":3,"area":null},{"id":"2605.15488","title":"SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference","created":"2026-05-15","cited_by_count":3,"area":null},{"id":"2606.17516","title":"FoundCause: Causal Discovery with Latent Confounders from Observational Data","created":"2026-06-16","cited_by_count":3,"area":null},{"id":"2509.23695","title":"Estimating Time Series Foundation Model Transferability via In-Context Learning","created":"2025-09-28","cited_by_count":2,"area":null},{"id":"2511.04469","title":"Towards Causal Market Simulators","created":"2025-11-06","cited_by_count":2,"area":null},{"id":"2604.13332","title":"Selecting Feature Interactions for Generalized Additive Models by Distilling Foundation Models","created":"2026-04-14","cited_by_count":2,"area":null},{"id":"2605.10616","title":"MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image","created":"2026-05-11","cited_by_count":2,"area":null},{"id":"2606.19361","title":"Computational Identifiability","created":"2026-06-08","cited_by_count":2,"area":null},{"id":"2606.26467","title":"A Causal Foundation Model for Structure and Outcome Prediction","created":"2026-06-25","cited_by_count":2,"area":null},{"id":"2607.11508","title":"CDFM: Towards a General-Purpose Causal Discovery Foundation Model","created":"2026-07-13","cited_by_count":2,"area":null},{"id":"2607.23447","title":"PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling","created":"2026-07-26","cited_by_count":2,"area":null},{"id":"2607.27263","title":"DoTime: A Synthetic Benchmark Generator for Interventional and Counterfactual Time Series","created":"2026-07-29","cited_by_count":2,"area":null},{"id":"2608.17957","title":"Understanding the Surprising Generalization Properties of Tabular Foundation Models","created":"2026-08-18","cited_by_count":2,"area":null},{"id":"2608.20841","title":"Foundation Models for Partial Causal Identification","created":"2026-08-21","cited_by_count":2,"area":null},{"id":"2601.17869","title":"On the Emergence and Test-Time Use of Structural Information in Large Language Models","created":"2026-01-25","cited_by_count":1,"area":null},{"id":"2605.22335","title":"Learning Causal Orderings for In-Context Tabular Prediction","created":"2026-05-21","cited_by_count":1,"area":null},{"id":"2606.03332","title":"Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference","created":"2026-06-02","cited_by_count":1,"area":null},{"id":"2606.21212","title":"DCD-PFN: A Decoupling-Aware Foundation Model for Causal Discovery","created":"2026-06-19","cited_by_count":1,"area":null},{"id":"2609.03003","title":"Causal Foundation Models","created":"2026-09-02","cited_by_count":1,"area":null},{"id":"2512.00969","title":"Integrating a Causal Foundation Model into a Prescriptive Maintenance Framework for Optimising Production-Line OEE","created":"2025-11-30","cited_by_count":0,"area":null},{"id":"2601.18500","title":"Nearly Optimal Bayesian Inference for Structural Missingness","created":"2026-01-26","cited_by_count":0,"area":null},{"id":"2604.23107","title":"MOCA: A Transformer-based Modular Causal Inference Framework with One-way Cross-attention and Cutting Feedback","created":"2026-04-25","cited_by_count":0,"area":null},{"id":"2605.05993","title":"TabCF: Distributional Control Function Estimation with Tabular Foundation Models","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.08786","title":"PRIM: Meta-Learned Bayesian Root Cause Analysis","created":"2026-05-09","cited_by_count":0,"area":null},{"id":"2605.12308","title":"In-context learning to predict critical transitions in dynamical systems","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2605.29411","title":"The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction","created":"2026-05-28","cited_by_count":0,"area":null},{"id":"2606.06458","title":"In-Context Multiple Instance Learning","created":"2026-06-04","cited_by_count":0,"area":null},{"id":"2606.20889","title":"Temporal Causal Prior-Data Fitted Networks for Panel Data with Learned Reliability Signals","created":"2026-06-18","cited_by_count":0,"area":null},{"id":"2606.30336","title":"FlexTab: A Flexible Encoder-Decoder Architecture for In-Context Learning Across Diverse Tabular Tasks","created":"2026-06-29","cited_by_count":0,"area":null},{"id":"2609.06941","title":"When and Why LLM Causal Priors Help: Closed-Loop Prior Selection for Amortized Causal Inference","created":"2026-09-07","cited_by_count":0,"area":null},{"id":"2609.32167","title":"CAFE: Counterfactual Prediction via Fast Posterior Estimation","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2609.36881","title":"What You Observe Determines How You Identify Causal Effects: Evaluating Causal Models across Observational Views","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2609.39523","title":"CIDER-FM: Foundation Models for Causal Inference from Diverse Experimental Regimes","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.13764","title":"How Well Do Prior-Data Fitted Networks Amortize Bayesian Inference?","created":"2026-10-09","cited_by_count":0,"area":null},{"id":"2610.14770","title":"Prior-Fitted and Frozen Scorers for Multimodal Contrastive Learning","created":"2026-10-10","cited_by_count":0,"area":null}],"next_cursor":null}