5arXiv cs.LG (Machine Learning)·
Bloomberg releases Causal-TS: open-source Python library for causal discovery in time series
Bloomberg has open-sourced Causal-TS, a Python library for causal discovery in high-dimensional and nonstationary multivariate time series. The library implements four specialized algorithms (CDNOTS, CDNOTS+, CEDAR, GRACE) plus wrappers for established methods, with a unified conditional independence test layer GPU-accelerated via PyTorch. It includes a regime discovery pipeline for structural breaks, synthetic data generators, CLI tooling, and optional DoWhy integration for end-to-end causal effect estimation.
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PCMCI+ extended for causal discovery on irregular time series
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.
CausaLab: Scalable Benchmark for Interactive Causal Discovery by LLM Agents
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.
teLLMe: LLM-powered exploratory causal analysis system for urban driving data
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.
DKCD: Domain knowledge-enhanced causal discovery from unstructured data using LLMs
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.
Xaira Therapeutics on causal AI models and X-Cell for drug discovery
Latent Space interviews Bo Wang (Chief Discovery Officer) and Ci Chu (Chief AI Scientist) at Xaira Therapeutics about their X-Cell model and their strategy of generating proprietary data to train causal AI models for drug discovery. The discussion centers on why causal models require causal data and how Xaira is investing heavily in data generation as a foundation for model building. This is a substantive look at how a well-funded biotech is operationalizing AI-native drug discovery at scale.
CausalForge: Agentic framework for automated causal inference research grounded in Lean formal proofs
CausalForge is a new agentic research framework that automates theoretical research in causal inference by grounding outputs in the Lean proof assistant rather than relying on LLM reviewers, which the authors note accept fabricated papers at near-chance rates. The system combines Causalean, a 7,035-declaration Lean library for causal inference built with LLM assistance under human oversight, with CausalSmith, a self-improving pipeline that selects topics, proposes results, formalizes statements, and constructs machine-checked proofs. A statement audit step bridges the gap between formal correctness and scientific validity by comparing each theorem against its informal intended claim. The framework represents a notable approach to closing the automated research loop with formal verification rather than LLM judgment.
BODHI: Contrastive embedding training for causal discovery in Large Behavioural Models
Researchers identify a critical failure mode in biomedical language model embeddings: off-the-shelf encoders (BioBERT, PubMedBERT, BioM-ELECTRA) assign high cosine similarity (0.76–0.92) to causally unrelated cross-domain pairs, achieving 0% accuracy on cross-domain discrimination. The paper introduces BODHI, a contrastive training approach using hard negatives mined from a biomedical knowledge graph, which improves within-vs-across-domain separation from 1.05x to 2.30x and raises discrimination gap by +0.392. The work targets Large Behavioural Models (LBMs)—foundation models that reason over personal life graphs—where false embedding proximity directly produces false causal edges. Additional contributions include an OpenVINO inference optimization achieving 133x latency reduction (1367ms to 10ms) on Intel AMX hardware, plus a counterintuitive finding that FP16 outperforms INT8 on this silicon.
TiRex-2: xLSTM-based time series foundation model for multivariate and streaming forecasting
TiRex-2 is a recurrent xLSTM-based time series foundation model that extends the univariate TiRex to multivariate forecasting with past and future covariates, while supporting streaming inference at constant per-patch cost. The model uses a bidirectional time mixer and asymmetric grouped-attention variate mixer to handle future-known covariates without violating causality over target variables. A synthetic coupling pipeline enables scalable multivariate pretraining from univariate corpora. TiRex-2 claims state-of-the-art zero-shot performance on GIFT-Eval and fev-bench benchmarks with 38.4M–82.5M parameters depending on mode.