Cover Image

Front-door Reducibility: Reducing ADMGs to the Standard Front-door Setting via a Graphical Criterion

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mao, Jianqiao, Little, Max A.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning G.3; G.2.2; I.2.6
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning Nonlinear Regime Transitions via Semi-Parametric State-Space Models
by: Hiremath, Prakul Sunil
Published: (2026)

Unified Unbiased Variance Estimation for Maximum Mean Discrepancy: Robust Finite-Sample Performance with Imbalanced Data and Exact Acceleration under Null and Alternative Hypotheses
by: Zhong, Shijie, et al.
Published: (2026)

Optimizing Job Allocation using Reinforcement Learning with Graph Neural Networks
by: Quaedvlieg, Lars C. P. M.
Published: (2025)

Graph Coloring for Multi-Task Learning
by: Patapati, Santosh
Published: (2025)

Improving ML Training Data with Gold-Standard Quality Metrics
by: Barrett, Leslie, et al.
Published: (2025)

On the use of graph models to achieve individual and group fairness
by: Pérez-Peralta, Arturo, et al.
Published: (2026)

Graph Neural Networks are Heuristics
by: Min, Yimeng, et al.
Published: (2026)

Causal Cartographer: From Mapping to Reasoning Over Counterfactual Worlds
by: Gendron, Gaël, et al.
Published: (2025)

Off-policy Evaluation with Deeply-abstracted States
by: Hao, Meiling, et al.
Published: (2024)

Variational Search Distributions
by: Steinberg, Daniel M., et al.
Published: (2024)

The Reasoning-Creativity Trade-off: Toward Creativity-Driven Problem Solving
by: Luyten, Max Ruiz, et al.
Published: (2026)

CART-ELC: Oblique Decision Tree Induction via Exhaustive Search
by: Laack, Andrew D.
Published: (2025)

Mechanism Learning: reverse causal inference in the presence of multiple unknown confounding through causally weighted Gaussian mixture models
by: Mao, Jianqiao, et al.
Published: (2024)

Scalable and Interpretable Scientific Discovery via Sparse Variational Gaussian Process Kolmogorov-Arnold Networks (SVGP KAN)
by: Ju, Y. Sungtaek
Published: (2025)

Uncertainty Propagation Networks for Neural Ordinary Differential Equations
by: Jahanshahi, Hadi, et al.
Published: (2025)

SmallML: Bayesian Transfer Learning for Small-Data Predictive Analytics
by: Leontev, Semen
Published: (2025)

How Does the Pretraining Distribution Shape In-Context Learning? Task Selection, Generalization, and Robustness
by: Azizian, Waïss, et al.
Published: (2025)

Enhanced Random Subspace Local Projections for High-Dimensional Time Series Analysis
by: Khalid, Eman, et al.
Published: (2026)

HAPEns: Hardware-Aware Post-Hoc Ensembling for Tabular Data
by: Maier, Jannis, et al.
Published: (2026)

Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems
by: Pal, Biplab, et al.
Published: (2026)

The Role of Causal Features in Strategic Classification for Robustness and Alignment
by: Gois, Antonio, et al.
Published: (2026)

CLGNN: A Contrastive Learning-based GNN Model for Betweenness Centrality Prediction on Temporal Graphs
by: Zhang, Tianming, et al.
Published: (2025)

MR-GNF: Multi-Resolution Graph Neural Forecasting on Ellipsoidal Meshes for Efficient Regional Weather Prediction
by: Shchur, Andrii, et al.
Published: (2026)

An objective function for order preserving hierarchical clustering
by: Bakkelund, Daniel
Published: (2021)

From Theory to Practice with RAVEN-UCB: Addressing Non-Stationarity in Multi-Armed Bandits through Variance Adaptation
by: Fang, Junyi, et al.
Published: (2025)

Primal-Dual Sample Complexity Bounds for Constrained Markov Decision Processes with Multiple Constraints
by: Buckley, Max, et al.
Published: (2025)

Optimization-Free Topological Sort for Causal Discovery via the Schur Complement of Score Jacobians
by: Wu, Rui, et al.
Published: (2026)

Uncertainty Quantification for Scientific Machine Learning using Sparse Variational Gaussian Process Kolmogorov-Arnold Networks (SVGP KAN)
by: Ju, Y. Sungtaek
Published: (2025)

Bayesian Optimization in Linear Time
by: Schneider, Jesse, et al.
Published: (2026)

Spatial Shortcuts in Graph Neural Controlled Differential Equations
by: Detzel, Michael, et al.
Published: (2024)

TrajGPT: Controlled Synthetic Trajectory Generation Using a Multitask Transformer-Based Spatiotemporal Model
by: Hsu, Shang-Ling, et al.
Published: (2024)

On the approximation capability of GNNs in node classification/regression tasks
by: D'Inverno, Giuseppe Alessio, et al.
Published: (2021)

Sinusoidal Initialization, Time for a New Start
by: Fernández-Hernández, Alberto, et al.
Published: (2025)

S$^3$: Structured Sparsity Specification
by: Ghriss, Ayoub
Published: (2026)

Assessing the Performance-Efficiency Trade-off of Foundation Models in Probabilistic Electricity Price Forecasting
by: Lettner, Jan Niklas, et al.
Published: (2026)

Randomized Approach to Matrix Completion: Applications in Recommendation Systems and Image Inpainting
by: Krajewska, Antonina, et al.
Published: (2024)

Closing the Curvature Gap: Full Transformer Hessians and Their Implications for Scaling Laws
by: Petrov, Egor, et al.
Published: (2025)

Uncertainty Quantification for Reduced-Order Surrogate Models Applied to Cloud Microphysics
by: Katona, Jonas E., et al.
Published: (2025)

Improved Exploration in GFlownets via Enhanced Epistemic Neural Networks
by: Muhammad, Sajan, et al.
Published: (2025)

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study
by: Gabriel, Franck, et al.
Published: (2025)