研究人员开发了新的因果推断和发现方法，解决了潜在变量和连续时间序列数据带来的挑战。一种方法，Observable Neural ODEs (ObsNODEs)，通过从观测中重建潜在状态来实现因果预测。另一个框架DIRECT使用神经组学习具有生物学上可行的局部可塑性的定向因果影响，为因果声明提供了一个可审计的机制。此外，一个名为TrialCalibre的多智能体系统旨在自动化和扩展真实世界证据研究的因果推断工作流程，提高其可信度。 AI
影响 因果推断技术的进步可能导致更强大、更可解释的AI系统，特别是在需要理解因果关系的领域。
排序理由 多篇arXiv论文介绍了因果推断和发现的新颖方法。
AI 生成摘要 · Google Gemini · 来自 24 个来源。 我们如何撰写摘要 →
arXiv:2602.07915v2 Announce Type: replace-cross Abstract: Causal discovery from time series is a fundamental task in machine learning. However, its widespread adoption is hindered by a reliance on untestable causal assumptions and by the lack of robustness-oriented evaluation in …
arXiv:2604.26070v1 Announce Type: new Abstract: Causal inference in continuous-time sequential decision problems is challenged by hidden confounders. We show that, in latent state-space models with time-varying interventions, observability of the latent dynamics from observed dat…
arXiv:2604.26919v1 Announce Type: cross Abstract: Can Neural Assemblies -- groups of neurons that fire together and strengthen through co-activation -- learn the direction of causal influence between variables? While established as a computationally general substrate for classifi…
Causal inference is essential for data-driven decision-making, as it aims to uncover causal relationships from observational data. However, identifying causality remains challenging due to the potential for confounding and the distinction between correlation and causation. While …
Can Neural Assemblies -- groups of neurons that fire together and strengthen through co-activation -- learn the direction of causal influence between variables? While established as a computationally general substrate for classification, parsing, and planning, neural assemblies h…
Can Neural Assemblies -- groups of neurons that fire together and strengthen through co-activation -- learn the direction of causal influence between variables? While established as a computationally general substrate for classification, parsing, and planning, neural assemblies h…
Real-world evidence (RWE) studies that emulate target trials increasingly inform regulatory and clinical decisions, yet residual, hard-to-quantify biases still limit their credibility. The recently proposed BenchExCal framework addresses this challenge via a two-stage Benchmark, …
arXiv:2502.04274v4 Announce Type: replace Abstract: End-to-end representation learning has become a powerful tool for estimating causal quantities from high-dimensional observational data, but its efficiency remained unclear. Here, we face a central tension: End-to-end representa…
arXiv:2604.22416v1 Announce Type: new Abstract: Latent variables pose a fundamental challenge to causal discovery and inference. Conventional local methods focus on direct neighbors but fail to provide macro level insights. Cluster level methods enable macro causal reasoning but …
Latent variables pose a fundamental challenge to causal discovery and inference. Conventional local methods focus on direct neighbors but fail to provide macro level insights. Cluster level methods enable macro causal reasoning but either assume clusters are known a priori or req…
arXiv:2604.27307v1 Announce Type: new Abstract: Causal inference is essential for data-driven decision-making, as it aims to uncover causal relationships from observational data. However, identifying causality remains challenging due to the potential for confounding and the disti…
arXiv:2604.26820v1 Announce Type: new Abstract: Detectors often suffer from degraded performance, primarily due to the distributional gap between the source and target domains. This issue is especially evident in single-source domains with limited data, as models tend to rely on …
Causal inference is essential for data-driven decision-making, as it aims to uncover causal relationships from observational data. However, identifying causality remains challenging due to the potential for confounding and the distinction between correlation and causation. While …
Detectors often suffer from degraded performance, primarily due to the distributional gap between the source and target domains. This issue is especially evident in single-source domains with limited data, as models tend to rely on confounders (e.g., illumination, co-occurrence, …
arXiv:2604.25157v1 Announce Type: cross Abstract: Data assimilation (DA) integrates observational information with model predictions to improve state estimation in complex systems. While filtering provides the basis for online forecasts by using only past and present observations…
arXiv:2602.01433v2 Announce Type: replace-cross Abstract: Multivariate time series in domains such as finance, climate science, and healthcare often exhibit long-term trends, seasonal patterns, and short-term fluctuations, complicating causal inference under non-stationarity and …
arXiv:2604.23904v1 Announce Type: cross Abstract: Synthetic data offers a promising tool for privacy-preserving data release, augmentation, and simulation, but its use in causal inference requires preserving more than predictive fidelity. We show that fully generative tabular syn…
arXiv:2604.23107v1 Announce Type: new Abstract: Causal effect estimation from observational data requires careful adjustment for confounding. Classical estimators such as inverse probability weighting and augmented inverse probability weighting are effective under favorable model…
Data assimilation (DA) integrates observational information with model predictions to improve state estimation in complex systems. While filtering provides the basis for online forecasts by using only past and present observations, it can exhibit delays and biases when the underl…
Synthetic data offers a promising tool for privacy-preserving data release, augmentation, and simulation, but its use in causal inference requires preserving more than predictive fidelity. We show that fully generative tabular synthesizers, including GAN- and LLM-based models, ca…
Causal effect estimation from observational data requires careful adjustment for confounding. Classical estimators such as inverse probability weighting and augmented inverse probability weighting are effective under favorable model specification, but may become unstable when tre…
Determining identifiability of causal effects from observational data under latent confounding is a central challenge in causal inference. For linear structural causal models, identifiability of causal effects is decidable through symbolic computation. However, standard approache…
<p>With all the LLM hype, it’s worth remembering that enterprise stakeholders want answers to “why” questions. Enter causal inference. Paul Hünermund has been doing research and writing on this topic for some time and joins us to introduce the topic. He also shares some relevant …
<p>Lucy D’Agostino McGowan, cohost of the Casual Inference Podcast and a professor at Wake Forest University, joins Daniel and Chris for a deep dive into causal inference. Referring to current events (e.g. misreporting of COVID-19 data in Georgia) as examples, they explore how we…