Let Time Tell: Identification and Gaussian Process Estimation for Interrupted Time Series
Read the original on arXiv Statistics ML →The Flow has not summarised this story yet — read it at arXiv Statistics ML.
The Flow has not summarised this story yet — read it at arXiv Statistics ML.
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.