arXiv:2607. 27263v1 Announce Type: new Abstract: Most benchmarks for causal inference over time series are observational, small, or domain-specific, leaving interventional and counterfactual estimation under-served exactly where it matters most, such as in healthcare, policy evaluation, and climate science.
The monograph explores the relationships between Gaussian processes and reproducing kernel Hilbert spaces (RKHS), two widely used approaches that rely on positive definite kernels. It examines how these frameworks connect and are equivalent across key topics such as regression, interpolation, numerical integration, distributional discrepancies, statistical dependence, and Gaussian process sample path properties. By establishing a unifying perspective based on the equivalence between the Gaussian Hilbert space and the RKHS, the work aims to bridge methods developed independently by the machine learning, statistics, and numerical analysis communities.
arXiv:2410. 14483v3 Announce Type: replace-cross Abstract: Reliable uncertainty quantification for causal effects is crucial in high-stakes applications, but remains challenging when the target is an entire function rather than a scalar estimand.
arXiv:2604.26070v3 Announce Type: replace Abstract: Causal inference in continuous-time sequential decision problems is challenged by hidden confounding and partially observed states. We show that, u...
The Time-partitioning Gaussian Process Mixture Model (TPGPMM) is a Bayesian nonparametric framework that identifies persistent regimes and models nonstationary dependence in time series. It uses an adaptive Pitman–Yor prior on contiguous segmentations combined with regime-specific Gaussian processes, and learns change points and hyperparameters via a split–merge Metropolis-within-Gibbs sampler. Experiments on the CBOE Volatility Index and the 10‑year U.S. Treasury yield recover regime structures aligned with major market and monetary‑policy events, while variants that estimate observation noise improve predictive accuracy but may reduce sparsity or event alignment.
arXiv:2606. 07399v1 Announce Type: cross Abstract: Generative models for counterfactual outcomes have great potential to support decision-making under complex interventions, but existing approaches are limited by unstable estimation, poor generalization across environments, and bias from nuisance model misspecification.
arXiv:2610.03201v1 Announce Type: cross Abstract: Gaussian Processes (GPs) are a powerful tool for modelling and quantifying uncertainty in functional relationships. However, they require practitione...
The paper proposes a new approach to window‑level monitoring of temporal processes by treating it as a reference‑based hypothesis test. Instead of relying on predefined parametric models, the method uses an empirical reference distribution derived from task‑ or domain‑specific data, combined with pretrained time‑series encoders, kernel density estimation, and conformal calibration to provide finite‑sample valid inference in a learned representation space. Classical concepts such as stationarity and cyclostationarity naturally emerge as special cases of this framework, and experiments show the method’s sensitivity to distributional changes while maintaining well‑calibrated inference under stable conditions.
arXiv:2610.08689v1 Announce Type: new Abstract: Counterfactual inference in Gaussian-process structural causal models (GP-SCMs) has been developed primarily for continuous endogenous variables, limit...
The paper introduces a method for attributing changes in alarm scores in industrial and scientific systems by modeling the problem as a Shapley game over hybrid structural causal models. It distinguishes between changes in structural functions and exogenous laws, providing a detailed, function/law-resolved explanation of how each type of change propagates to the alarm score. For ordered linear SCMs and quadratic scores, the authors present a polynomial‑time exact computation of the full Shapley vector and, for scarce incident data, a cross‑fitted estimation approach with error bounds and top‑k recovery guarantees, validated through experiments on controlled and real shifts.
arXiv:2607. 10926v1 Announce Type: new Abstract: Identifying heterogeneous treatment effects under unobserved confounding is central in observational causal inference.
arXiv:2605. 12410v2 Announce Type: replace-cross Abstract: We propose and analyze a model-based bootstrap for transition kernels in finite controlled Markov chains (CMCs) with possibly nonstationary or history-dependent control policies, a setting that arises naturally in offline reinforcement learning (RL) when the behavior policy generating the data is unknown.