A new arXiv preprint proposes an extension of PCMCI+, a leading causal discovery method for multivariate time series, to handle irregularly sampled data by aggregating causal influence over temporal windows rather than fixed lags. The method is evaluated on synthetic irregular event streams with known causal structures across varying signal-to-noise ratios, consistently recovering the underlying causal graph and outperforming standard PCMCI+. This addresses a practical gap for domains like healthcare, sensor networks, and financial transactions where regular sampling is not guaranteed.
A new arXiv preprint proposes a weaker assumption called 'intervention-immediacy faithfulness' that allows causal discovery to proceed correctly even when buffering or stabilizing pathways cancel out and violate the standard faithfulness assumption. The authors show that hard interventions alone can nonparametrically identify causal structures, repositioning interventions as the primary information source over conditional independence testing on observational data. The work also characterizes equivalence classes when interventions are insufficient to fully identify structure.
CausaLab is a new evaluation environment that tests LLM agents on interactive causal discovery tasks, requiring them to recover both causal graphs and structural equations from synthetic laboratory episodes governed by randomly sampled structural causal models (SCMs). The benchmark separates predictive accuracy from genuine causal understanding, revealing a persistent gap: GPT-5.2-high achieves 92% task accuracy in a 6-node observational setting but only 0.471 all-edge F1 for mechanism recovery. Mixed observation-intervention strategies improve structural fidelity, while pure intervention strategies underperform on both metrics. Premature stopping is identified as a key agent weakness, partially mitigated by prompting models to verify hypothesis-data consistency.
Researchers introduce DKCD, a framework that augments LLM-based causal discovery with domain-specific knowledge retrieval and guided reasoning to address two failure modes: insufficient identification of latent causal factors and unreliable factor annotation. The system chains knowledge mining, knowledge-guided causal reasoning, and causal structure discovery into a pipeline that produces causal graphs from unstructured text in high-expertise domains like healthcare and finance. Experiments on two domain-specific datasets show improvements over baselines on both factor identification and graph construction quality.
Researchers introduce RMISC, an openly accessible corpus of ~200 datasets and 142 billion time points of real-world multivariate time series data, designed to address the gap between synthetic and real-world pretraining data for time series foundation models (TSFMs). Four advanced TSFMs are pretrained on univariate, synthetic multivariate, and real-world multivariate data and evaluated on zero-shot generalization benchmarks. Results show that real-world multivariate pretraining data consistently improves generalization for both univariate and multivariate TSFMs. The work provides both a reusable dataset resource and empirical evidence on the synthetic-vs-real data question for time series modeling.
CausalMix proposes treating data mixture optimization for LLM training as a causal inference problem, using Conditional Average Treatment Effect (CATE) estimation to infer optimal domain mixtures without costly retraining when the data pool changes. The method fits a causal model on 512 runs of Qwen2.5-0.5B and extrapolates the resulting mixture to train a 7B model, also generalizing to long chain-of-thought data on Qwen3-4B-Base. It outperforms RegMix and other baselines across multiple downstream tasks while providing interpretable visual analysis of mixing strategies via a CATE Interpreter. The approach addresses a practical scalability limitation in existing proxy-model-based mixture methods.
Researchers introduce teLLMe, a system that combines causal structure learning (PC algorithm, DoWhy) with a schema-aware LLM to answer natural-language causal queries over urban driving datasets derived from dashcam annotations. Users pose questions like 'How would rain change traffic density?' and receive a structured 'Causal Card' summarizing effect estimates, adjustment sets, DAG support, and uncertainty. Case studies on BDD-derived traffic events demonstrate plausible causal relationships involving weather and peak hours. The system is framed as a hypothesis-generation tool rather than a definitive causal inference engine.
FM-CGM is a modular framework that decomposes visual causal reasoning into three components—concept extractor, concept manipulator, and counterfactual generator—using pretrained foundation models without task-specific causal training. The approach combines a large reasoning model for causal inference with a text-to-image diffusion model for generation, enabling zero-shot causal discovery and counterfactual image synthesis. A novel cross-attention mechanism called Causal Semantic Guidance (CSG) ensures that semantic interventions propagate correctly through causal descendants while preserving unaffected image regions. Empirical results show the framework can identify plausible causal structures and generate faithful counterfactual images.
A new arXiv preprint introduces a model-agnostic empirical framework for auditing synthetic data generated by LLMs and generative AI systems for privacy leakage. The framework distinguishes 'true disclosures' (direct reproduction of user data) from 'phantom disclosures' (incidental generation), using held-out control sets and statistical hypothesis testing without requiring model access, canary insertion, or shadow model training. It functions as a membership inference attack and provides empirical lower bounds on privacy leakage that are tighter than prior data-based auditing methods. The approach is computationally lightweight and applicable to any synthetic data generation mechanism.