{"list":"other","total":66,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"id":"2207.01848","title":"TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second","created":"2022-07-05","cited_by_count":674},{"id":"1510.04342","title":"Estimation and Inference of Heterogeneous Treatment Effects using Random Forests","created":"2015-10-14","cited_by_count":600},{"id":"1706.03461","title":"Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning","created":"2017-06-12","cited_by_count":478},{"id":"2112.10510","title":"Transformers Can Do Bayesian Inference","created":"2021-12-20","cited_by_count":360},{"id":"2502.05564","title":"TabICL: A Tabular Foundation Model for In-Context Learning on Large Data","created":"2025-02-08","cited_by_count":289},{"id":"2002.02770","title":"A Survey on Causal Inference","created":"2020-02-05","cited_by_count":202},{"id":"1704.02801","title":"Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes","created":"2017-04-10","cited_by_count":158},{"id":"2602.11139","title":"TabICLv2: A better, faster, scalable, and open tabular foundation model","created":"2026-02-11","cited_by_count":144},{"id":"2410.18164","title":"TabDPT: Scaling Tabular Foundation Models on Real Data","created":"2024-10-23","cited_by_count":113},{"id":"1911.04706","title":"FLAML: A Fast and Lightweight AutoML Library","created":"2019-11-12","cited_by_count":106},{"id":"2311.01933","title":"ForecastPFN: Synthetically-Trained Zero-Shot Forecasting","created":"2023-11-03","cited_by_count":104},{"id":"1902.00981","title":"Learning Counterfactual Representations for Estimating Individual Dose-Response Curves","created":"2019-02-03","cited_by_count":90},{"id":"2205.12934","title":"Amortized Inference for Causal Structure Learning","created":"2022-05-25","cited_by_count":90},{"id":"2510.21204","title":"Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models","created":"2025-10-24","cited_by_count":77},{"id":"2204.04875","title":"Learning to Induce Causal Structure","created":"2022-04-11","cited_by_count":68},{"id":"2305.10032","title":"A Survey on Causal Discovery: Theory and Practice","created":"2023-05-17","cited_by_count":63},{"id":"2506.06039","title":"Do-PFN: In-Context Learning for Causal Effect Estimation","created":"2025-06-06","cited_by_count":51},{"id":"2506.07918","title":"CausalPFN: Amortized Causal Effect Estimation via In-Context Learning","created":"2025-06-09","cited_by_count":49},{"id":"2011.15007","title":"RealCause: Realistic Causal Inference Benchmarking","created":"2020-11-30","cited_by_count":46},{"id":"1607.00699","title":"The State of Applied Econometrics - Causality and Policy Evaluation","created":"2016-07-03","cited_by_count":41},{"id":"2507.03971","title":"Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data","created":"2025-07-05","cited_by_count":41},{"id":"2506.10707","title":"ConTextTab: A Semantics-Aware Tabular In-Context Learner","created":"2025-06-12","cited_by_count":34},{"id":"2506.10914","title":"Foundation Models for Causal Inference via Prior-Data Fitted Networks","created":"2025-06-12","cited_by_count":32},{"id":"2502.16294","title":"TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data","created":"2025-02-22","cited_by_count":29},{"id":"2211.01939","title":"Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation","created":"2022-11-03","cited_by_count":25},{"id":"2412.16577","title":"A Meta-Learning Approach to Bayesian Causal Discovery","created":"2024-12-21","cited_by_count":22},{"id":"2402.01929","title":"Sample, estimate, aggregate: A recipe for causal discovery foundation models","created":"2024-02-02","cited_by_count":21},{"id":"2301.12292","title":"Zero-shot causal learning","created":"2023-01-28","cited_by_count":17},{"id":"2310.00809","title":"Towards Causal Foundation Model: on Duality between Causal Inference and Attention","created":"2023-10-01","cited_by_count":17},{"id":"2603.03805","title":"Relational In-Context Learning via Synthetic Pre-training with Structural Prior","created":"2026-03-04","cited_by_count":14},{"id":"2503.05985","title":"Black Box Causal Inference: Effect Estimation via Meta Prediction","created":"2025-03-07","cited_by_count":13},{"id":"2507.05526","title":"Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning","created":"2025-07-07","cited_by_count":13},{"id":"2405.16924","title":"Demystifying amortized causal discovery with transformers","created":"2024-05-27","cited_by_count":11},{"id":"2411.02771","title":"Doubly robust inference via calibration","created":"2024-11-05","cited_by_count":9},{"id":"2601.22259","title":"Tabular Foundation Models Can Do Survival Analysis","created":"2026-01-29","cited_by_count":9},{"id":"2411.01295","title":"Marginal Causal Flows for Validation and Inference","created":"2024-11-02","cited_by_count":8},{"id":"2602.14972","title":"Use What You Know: Causal Foundation Models with Partial Graphs","created":"2026-02-16","cited_by_count":7},{"id":"2603.11090","title":"Interventional Time Series Priors for Causal Foundation Models","created":"2026-03-11","cited_by_count":7},{"id":"2603.29475","title":"Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks","created":"2026-03-31","cited_by_count":6},{"id":"2608.01400","title":"TabDPT-Turbo: Efficient In-Context Learning for Tabular Prediction","created":"2026-08-02","cited_by_count":6},{"id":"2605.07204","title":"Arrow: A Foundation Model for Causal Discovery","created":"2026-05-08","cited_by_count":5},{"id":"2506.18285","title":"Learning Causal Graphs at Scale: A Foundation Model Approach","created":"2025-06-23","cited_by_count":4},{"id":"2603.12037","title":"Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference","created":"2026-03-12","cited_by_count":4},{"id":"2605.15133","title":"Causal Foundation Models with Continuous Treatments","created":"2026-05-14","cited_by_count":4},{"id":"2605.28880","title":"Towards Continuous-time Causal Foundation Models","created":"2026-05-26","cited_by_count":4},{"id":"2605.31156","title":"TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery","created":"2026-05-29","cited_by_count":4},{"id":"2606.04564","title":"SurvPFN: Towards Foundation Models for Survival Predictions","created":"2026-06-03","cited_by_count":4},{"id":"2606.29241","title":"Towards Evaluating Data Priors for Tabular Foundation Models","created":"2026-06-28","cited_by_count":4},{"id":"2601.21092","title":"MapPFN: Learning Causal Perturbation Maps in Context","created":"2026-01-28","cited_by_count":3},{"id":"2605.10590","title":"Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networks","created":"2026-05-11","cited_by_count":3},{"id":"2605.12924","title":"IV-ICL: Bounding Causal Effects with Instrumental Variables via In-Context Learning","created":"2026-05-13","cited_by_count":3},{"id":"2605.15488","title":"SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference","created":"2026-05-15","cited_by_count":3},{"id":"2606.17516","title":"FoundCause: Causal Discovery with Latent Confounders from Observational Data","created":"2026-06-16","cited_by_count":3},{"id":"2608.16319","title":"Advancing Open and Reproducible Relational Learning: RelArena-$\\alpha$, TabPFN-Rel and RPI","created":"2026-08-17","cited_by_count":3},{"id":"2606.26467","title":"A Causal Foundation Model for Structure and Outcome Prediction","created":"2026-06-25","cited_by_count":2},{"id":"2607.11508","title":"CDFM: Towards a General-Purpose Causal Discovery Foundation Model","created":"2026-07-13","cited_by_count":2},{"id":"2607.11510","title":"DAG-FM: A Foundation Model for Causal Discovery under Heterogeneous Causal Mechanisms","created":"2026-07-13","cited_by_count":2},{"id":"2607.23447","title":"PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling","created":"2026-07-26","cited_by_count":2},{"id":"2608.20841","title":"Foundation Models for Partial Causal Identification","created":"2026-08-21","cited_by_count":2},{"id":"2512.11840","title":"Amortized Causal Discovery with Prior-Fitted Networks","created":"2025-12-03","cited_by_count":1},{"id":"2602.18662","title":"Large Causal Models for Temporal Causal Discovery","created":"2026-02-20","cited_by_count":1},{"id":"2606.05797","title":"Causal Longitudinal Prior-Fitted Networks for Counterfactual Outcome Prediction","created":"2026-06-04","cited_by_count":1},{"id":"2606.21212","title":"DCD-PFN: A Decoupling-Aware Foundation Model for Causal Discovery","created":"2026-06-19","cited_by_count":1}],"next_cursor":null}