Copertina

Time-Varying Home Field Advantage in Football: Learning from a Non-Stationary Causal Process

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Qi, Minhao, Cai, Hengrui, Hu, Guanyu, Shen, Weining
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Applications Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

How Much Does Home Field Advantage Matter in Soccer Games? A Causal Inference Approach for English Premier League Analysis
di: Wang, Chen, et al.
Pubblicazione: (2022)

Learning-to-Defer in Non-Stationary Time Series via Switching State-Space Models
di: Montreuil, Yannis, et al.
Pubblicazione: (2026)

Probabilistic Wind Power Modelling via Heteroscedastic Non-Stationary Gaussian Processes
di: Ladopoulou, Domniki, et al.
Pubblicazione: (2025)

Beyond Linearity and Time-Homogeneity: Relational Hyper Event Models with Time-Varying Non-Linear Effects
di: Boschi, Martina, et al.
Pubblicazione: (2025)

Time-Varying Parameters as Ridge Regressions
di: Coulombe, Philippe Goulet
Pubblicazione: (2020)

Efficient Causal Discovery for Autoregressive Time Series
di: Fesanghary, Mohammad, et al.
Pubblicazione: (2025)

Fusing Climate Data Products using a Spatially Varying Autoencoder
di: Johnson, Jacob A., et al.
Pubblicazione: (2024)

Efficient Causal Structure Learning via Modular Subgraph Integration
di: Sun, Haixiang, et al.
Pubblicazione: (2026)

Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experiments
di: Kovačević, Luka, et al.
Pubblicazione: (2024)

Causal Feature Learning in the Social Sciences
di: Huang, Jingzhou, et al.
Pubblicazione: (2025)

Sample Efficient Bayesian Learning of Causal Graphs from Interventions
di: Zhou, Zihan, et al.
Pubblicazione: (2024)

Spatially-Heterogeneous Causal Bayesian Networks for Seismic Multi-Hazard Estimation: A Variational Approach with Gaussian Processes and Normalizing Flows
di: Li, Xuechun, et al.
Pubblicazione: (2025)

A Bayesian Framework for Causal Analysis of Recurrent Events with Timing Misalignment
di: Oganisian, Arman, et al.
Pubblicazione: (2023)

SLEM: Machine Learning for Path Modeling and Causal Inference with Super Learner Equation Modeling
di: Vowels, Matthew J.
Pubblicazione: (2023)

Bayesian Causal Forests for Longitudinal Data: Assessing the Impact of Part-Time Work on Growth in High School Mathematics Achievement
di: McJames, Nathan, et al.
Pubblicazione: (2024)

Curious Causality-Seeking Agents Learn Meta Causal World
di: Zhao, Zhiyu, et al.
Pubblicazione: (2025)

Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutants
di: Xu, Gang, et al.
Pubblicazione: (2024)

Adaptive Degradation Process with Deep Learning-Driven Trajectory
di: Yang, Li
Pubblicazione: (2021)

Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data
di: Risser, Mark D., et al.
Pubblicazione: (2024)

Causal mediation analysis with one or multiple mediators: a comparative study
di: Abécassis, Judith, et al.
Pubblicazione: (2025)

What Influences the Field Goal Attempts of Professional Players? Analysis of Basketball Shot Charts via Log Gaussian Cox Processes with Spatially Varying Coefficients
di: Cao, Jiahao, et al.
Pubblicazione: (2025)

Adaptive Online Experimental Design for Causal Discovery
di: Elahi, Muhammad Qasim, et al.
Pubblicazione: (2024)

Causal machine learning for predicting treatment outcomes
di: Feuerriegel, Stefan, et al.
Pubblicazione: (2024)

From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies
di: Gutman, Rom, et al.
Pubblicazione: (2025)

Double Machine Learning for Adaptive Causal Representation in High-Dimensional Data
di: Aouar, Lynda, et al.
Pubblicazione: (2024)

High-Dimensional Statistical Process Control via Manifold Fitting and Learning
di: Tas, Burak I., et al.
Pubblicazione: (2025)

Data Curation for Machine Learning Interatomic Potentials by Determinantal Point Processes
di: Zou, Joanna, et al.
Pubblicazione: (2026)

Multidata Causal Discovery for Statistical Hurricane Intensity Forecasting
di: S, Saranya Ganesh, et al.
Pubblicazione: (2025)

Time-Varying Network Driver Estimation (TNDE) Quantifies Stage-Specific Regulatory Effects From Single-Cell Snapshots
di: Li, Jiaxin, et al.
Pubblicazione: (2025)

Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees
di: Janssen, Joseph, et al.
Pubblicazione: (2022)

Causal Responder Detection
di: Frostig, Tzviel, et al.
Pubblicazione: (2024)

Wafer-Level Etch Spatial Profiling for Process Monitoring from Time-Series with Time-LLM
di: Kim, Hyunwoo, et al.
Pubblicazione: (2026)

Deep Learning Surrogates for Real-Time Gas Emission Inversion
di: Newman, Thomas, et al.
Pubblicazione: (2025)

Integration of Multi-Mode Preference into Home Energy Management System Using Deep Reinforcement Learning
di: Sumayli, Mohammed, et al.
Pubblicazione: (2025)

Boosting Statistic Learning with Synthetic Data from Pretrained Large Models
di: Jiang, Jialong, et al.
Pubblicazione: (2025)

Age-Dependent Heterogeneity in the Association Between Physical Activity and Mental Distress: A Causal Machine Learning Analysis of 3.2 Million U.S. Adults
di: Shan, Yuan
Pubblicazione: (2026)

Explainable Federated Bayesian Causal Inference and Its Application in Advanced Manufacturing
di: Xiao, Xiaofeng, et al.
Pubblicazione: (2025)

Applications of Machine Learning in Pharmacogenomics: Clustering Plasma Concentration-Time Curves
di: Lautier, Jackson P., et al.
Pubblicazione: (2022)

A Causal Machine Learning Framework for Treatment Personalization in Clinical Trials: Application to Ulcerative Colitis
di: Minoccheri, Cristian, et al.
Pubblicazione: (2026)

Testing Generalizability in Causal Inference
di: Manela, Daniel de Vassimon, et al.
Pubblicazione: (2024)