Time-varying causal models provide a powerful framework for studying dynamic scientific systems, yet most existing approaches assume that the underlying causal network is known a priori - an assumption rarely satisfied in real-world domains where causal structure is uncertain, evolving, or only indirectly observable. This limits the applicability of dynamic causal inference in many scientific settings. We propose Dynamic Causal Network Autoregression (DCNAR), a two-stage neural causal modeling framework that integrates data-driven causal discovery with time-varying causal inference. In the first stage, a neural autoregressive causal discovery model learns a sparse directed causal network from multivariate time series. In the second stage, this learned structure is used as a structural prior for a time-varying neural network autoregression, enabling dynamic estimation of causal influence without requiring pre-specified network structure. We evaluate the scientific validity of DCNAR using behavioral diagnostics that assess causal necessity, temporal stability, and sensitivity to structural change, rather than predictive accuracy alone. Experiments on multi-country panel time-series data demonstrate that learned causal networks yield more stable and behaviorally meaningful dynamic causal inferences than coefficient-based or structure-free alternatives, even when forecasting performance is comparable. These results position DCNAR as a general framework for using AI as a scientific instrument for dynamic causal reasoning under structural uncertainty.

To test the efficiency of each component, we remove them separately (LG-ODE-no att,7 LG-ODE-no PE) and find the performances drop. This suggests that distinguishing the importance of nodes w.r.t8 time and incorporating temporal information via learnable positional encoding would benefit model performance.9 ForEqn2, we adopt the GNN model in[2]tocapture the interaction among agents.

Time series forecasting has applications across domains and industries, especially in healthcare, but the technical expertise required to analyze data, build models, and interpret results can be a barrier to using these techniques. This article presents a web platform that makes the process of analyzing and plotting data, training forecasting models, and interpreting and viewing results accessible to researchers and clinicians. Users can upload data and generate plots to showcase their variables and the relationships between them. The platform supports multiple forecasting models and training techniques which are highly customizable according to the user's needs. Additionally, recommendations and explanations can be generated from a large language model that can help the user choose appropriate parameters for their data and understand the results for each model. The goal is to integrate this platform into learning health systems for continuous data collection and inference from clinical pipelines.

Discrete event systems are present both in observations of nature, socio economical sciences, and industrial systems. Standard analysis approaches do not usually exploit their dual event / state nature: signals are either modeled as transition event sequences, emphasizing event order alignment, or as categorical or ordinal state timeseries, usually resampled a distorting and costly operation as the observation period and number of events grow. In this work we define state transition event timeseries (STE-ts) and propose a new Selective Temporal Hamming distance (STH) leveraging both transition time and duration-in-state, avoiding costly and distorting resampling on large databases. STH generalizes both resampled Hamming and Jaccard metrics with better precision and computation time, and an ability to focus on multiple states of interest. We validate these benefits on simulated and real-world datasets.

In regions like Punjab, India, where groundwater levels are plummeting at 41.6 cm/year, adopting water-saving rice farming practices is critical. Direct-Seeded Rice (DSR) and Alternate Wetting and Drying (A WD) can cut irrigation water use by 20-40% without hurting yields, yet lack of spatial data on adoption impedes effective adaptation policy and climate action. We present a machine learning framework to bridge this data gap by monitoring sustainable rice farming at scale. In collaboration with agronomy experts and a large-scale farmer training program, we obtained ground-truth data from 1,400 fields across Punjab. Leveraging this partnership, we developed a novel dimensional classification approach that decouples sowing and irrigation practices, achieving F1 scores of 0.8 and 0.74 respectively, solely employing Sentinel-1 satellite imagery. Explainability analysis reveals that DSR classification is robust while A WD classification depends primarily on planting schedule differences, as Sentinel-1's 12-day revisit frequency cannot capture the higher frequency irrigation cycles characteristic of A WD practices. Applying this model across 3 million fields reveals spatial heterogeneity in adoption at the state level, highlighting gaps and opportunities for policy targeting. Our district-level adoption rates correlate well with government estimates (Spearman's ρ=0.69 and Rank Biased Overlap=0.77). This study provides policymakers and sustainability programs a powerful tool to track practice adoption, inform targeted interventions, and drive data-driven policies for water conservation and climate mitigation at regional scale.

We thank the reviewers for their insightful feedback. In the following, we address their concerns and questions. ": There is a big misunderstanding. The table above shows that the proposed model performs best. ": Deep SVDD uses only one center and one layer, while we have multiple centers ( 's, the key challenge is on what contribution Indeed, these have been discussed at lines 115-116 and 126-128.

Encoding models predict brain responses to a set of given stimuli. Recently, deep neural networks have been used as encoding models to predict brain activity as recorded by functional MRI (fMRI) [1, 2, 3, 4, 5, 6]. These studies investigate whether representations in deep neural networks correspond to those in the human brain. This relationship is often assessed using linear models, with successful prediction taken as evidence of shared representational structure. Studies have investigated representations from both unimodal and multimodal deep neural networks, including large language models (LLMs) [2, 4, 7, 8], vision models [9, 10], audio models [1, 11], and video-language models (VLMs) [12], to predict brain activity. However, existing studies face challenges in generalizability and comparability. Differences in stimulus modality, quantity, and content, as well as in preprocessing and scoring, make cross-study comparisons difficult. The Algonauts 2025 Challenge [13] provides a framework to address these issues, offering an openly available, preprocessed dataset with a large amount of data per subject and aligned stimuli across modalities, including video, audio, and transcripts, along with a standardized evaluation procedure. The challenge places particular emphasis on generalizability, including both in-distribution and out-of-distribution test sets to rigorously evaluate how well models transfer to new stimuli. 1

We find that although Masking is very expensive, it does not perform well. We will include a more detailed discussion. S ( X) is network output i.e. S Upon your suggestion, we generated a new dataset with the advised setting. We will include this dataset along with more datasets generated using HMMs and state models in the benchmark. We will consider replacing GradientSHAP with SHAP in our final draft.