The paper introduces the Counterfactual Graph Neural Process (CFGNP), an end‑to‑end method that directly answers counterfactual queries from observational data without first estimating a causal graph. CFGNP employs Graph Neural Networks to leverage a dual‑graph structure, limiting cross‑world message passing to matched factual–counterfactual variables. Experiments on synthetic and image‑based datasets, including the German Loan benchmark, show that CFGNP outperforms existing methods, reducing mean‑squared error from $4.51 imes10^{-2}$ to $0.19 imes10^{-2}$, and generalizes from 10‑node to 30‑node graphs without retraining.
arXiv:2607. 14416v1 Announce Type: new Abstract: The interconnected nature of global financial systems makes them vulnerable to systemic risks, where the failure of a few institutions can trigger catastrophic cascading defaults.
arXiv:2606. 05972v1 Announce Type: new Abstract: Causal graphs provide a high-level language for making mechanisms transparent.
Structural causal models are the standard language for reasoning about interventions and counterfactuals, but they describe static variables, typically measured once, and usually forbid cyclic dependencies. Many systems we care about, such as patients, climates, and economies, instead evolve continuously in time, are observed at irregular time points, and contain feedback loops.
The paper presents a comparative analysis of six state‑of‑the‑art counterfactual explainers for graph neural networks, focusing on methods that can both add and remove edges to alter model predictions. It evaluates these explainers across diverse real‑world and synthetic datasets, covering binary and multi‑class graph and node classification tasks, using a range of quantitative and qualitative metrics. The study highlights the trade‑offs between explanation size, coverage, and quality, aiming to pinpoint each method’s strengths and weaknesses to inform future research.
arXiv:2606. 05797v1 Announce Type: new Abstract: Longitudinal treatment decisions require predicting potential outcomes under future treatment sequences in the presence of time-varying confounding, heterogeneous patient dynamics, and limited domain-specific data.
arXiv:2602. 14972v2 Announce Type: replace Abstract: Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions.
arXiv:2603. 24304v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) deliver strong performance on graph tasks, but their accuracy drops significantly under out-of-distribution (OOD) scenarios.
arXiv:2609.14124v1 Announce Type: cross Abstract: Medical image analysis is often hindered by biased datasets, which can lead to biased models and limited clinical applicability. A promising strategy...
arXiv:2608.23835v1 Announce Type: new Abstract: Real-world decision-making in public health and social science can greatly benefit from predictive models, yet translating predictions into effective i...
CausalProfiler is a synthetic benchmark generator designed to evaluate causal machine learning (Causal ML) methods more rigorously and transparently. It randomly samples causal models, data, queries, and ground truths based on explicit design choices across observation, intervention, and counterfactual reasoning levels, providing coverage guarantees and transparent assumptions. The authors demonstrate its utility by testing several state‑of‑the‑art methods under diverse conditions, both within and outside the identification regime, highlighting the insights CausalProfiler can reveal.
arXiv:2509. 01916v2 Announce Type: replace Abstract: Causal disentanglement from soft interventions is identifiable under the assumptions of linear interventional faithfulness and availability of both observational and interventional data.
arXiv:2609.06941v1 Announce Type: new Abstract: Causal effect estimation asks how an outcome would change under an intervention, and medicine, economics, and public policy all treat it as a foundatio...