Cover Image

Assessing the overall and partial causal well-specification of nonlinear additive noise models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Schultheiss, Christoph, Bühlmann, Peter
Format:	Preprint
Published:	2023
Subjects:	Methodology Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Causality-oriented robustness: exploiting general noise interventions
by: Shen, Xinwei, et al.
Published: (2023)

Ancestor regression in structural vector autoregressive models
by: Schultheiss, Christoph, et al.
Published: (2024)

Clustered random forests with correlated data for optimal estimation and inference under potential covariate shift
by: Young, Elliot H., et al.
Published: (2025)

Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation
by: Wang, Zhenyu, et al.
Published: (2023)

Causal inference using invariant prediction: identification and confidence intervals
by: Peters, Jonas, et al.
Published: (2015)

Causality-Inspired Robustness for Nonlinear Models via Representation Learning
by: Šola, Marin, et al.
Published: (2025)

Extrapolation-Aware Nonparametric Statistical Inference
by: Pfister, Niklas, et al.
Published: (2024)

Controlling for discrete unmeasured confounding in nonlinear causal models
by: Burauel, Patrick, et al.
Published: (2024)

Invariant Probabilistic Prediction
by: Henzi, Alexander, et al.
Published: (2023)

Learning linear acyclic causal model including Gaussian noise using ancestral relationships
by: Cai, Ming, et al.
Published: (2024)

Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning
by: Guo, Zijian, et al.
Published: (2022)

Domain Generalization and Adaptation in Intensive Care with Anchor Regression
by: Londschien, Malte, et al.
Published: (2025)

The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology
by: Gamella, Juan L., et al.
Published: (2024)

Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
by: Gamella, Juan L., et al.
Published: (2022)

Treatment Effect Estimation with Observational Network Data using Machine Learning
by: Emmenegger, Corinne, et al.
Published: (2022)

Nonparametric efficient inference for network quantile causal effects under partial interference
by: Cheng, Chao, et al.
Published: (2026)

A self-supervised learning approach for denoising autoregressive models with additive noise: finite and infinite variance cases
by: Banerjee, Sayantan, et al.
Published: (2025)

DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
by: Blöbaum, Patrick, et al.
Published: (2022)

Causal Invariance Learning via Efficient Nonconvex Optimization
by: Wang, Zhenyu, et al.
Published: (2024)

Optimistic search: Change point estimation for large-scale data via adaptive logarithmic queries
by: Kovács, Solt, et al.
Published: (2020)

C-XGBoost: A tree boosting model for causal effect estimation
by: Kiriakidou, Niki, et al.
Published: (2024)

Group selection and shrinkage: Structured sparsity for semiparametric additive models
by: Thompson, Ryan, et al.
Published: (2021)

Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning
by: Gu, Yihong, et al.
Published: (2024)

Score matching through the roof: linear, nonlinear, and latent variables causal discovery
by: Montagna, Francesco, et al.
Published: (2024)

Parameter identification in linear non-Gaussian causal models under general confounding
by: Tramontano, Daniele, et al.
Published: (2024)

Counterfactual identifiability beyond global monotonicity: non-monotone triangular structural causal models
by: Tan, Pengcheng, et al.
Published: (2026)

BARTSIMP: flexible spatial covariate modeling and prediction using Bayesian additive regression trees
by: Jiang, Alex Ziyu, et al.
Published: (2023)

Classification by sparse generalized additive models
by: Abramovich, Felix
Published: (2022)

Dynamical causality under invisible confounders
by: Yan, Jinling, et al.
Published: (2024)

Collaborative causal inference on distributed data
by: Kawamata, Yuji, et al.
Published: (2022)

Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks
by: Richards, Jordan, et al.
Published: (2022)

Targeting relative risk heterogeneity with causal forests
by: Shirvaikar, Vik, et al.
Published: (2023)

Separation-based distance measures for causal graphs
by: Wahl, Jonas, et al.
Published: (2024)

Mixture of partially linear experts
by: Hwang, Yeongsan, et al.
Published: (2024)

Two-sample comparison through additive tree models for density ratios
by: Awaya, Naoki, et al.
Published: (2025)

The two filter formula reconsidered: Smoothing in partially observed Gauss--Markov models without information parametrization
by: Tronarp, Filip
Published: (2025)

Assumption violations in causal discovery and the robustness of score matching
by: Montagna, Francesco, et al.
Published: (2023)

A projected nonlinear state-space model for forecasting time series signals
by: Donner, Christian, et al.
Published: (2023)

Seeded intervals and noise level estimation in change point detection: A discussion of Fryzlewicz (2020)
by: Kovács, Solt, et al.
Published: (2020)

A causal viewpoint on prediction model performance under changes in case-mix: discrimination and calibration respond differently for prognosis and diagnosis predictions
by: van Amsterdam, Wouter A. C.
Published: (2024)