The paper introduces a causal type discipline for sequential experiments that use AI-generated covariates. It defines a framework—including a versioned representation map, causal role classifier, claim-status filter, and estimand lock—to ensure that generated features are correctly classified as treatments, mediators, outcomes, or other roles, thereby preserving the intended causal estimand. The authors apply this framework to analyze compression bias, mediator adjustment, leakage, and other issues, demonstrating through simulations that careful refinement of generated covariates can reduce bias while design erasure or improper selection can lead to bias or undercoverage.
By Takes Fujita (VRI), Nobutaka Hattori (Department of Neurology, Juntendo University School of Medicine)
The paper introduces information set emulation, a method that attaches detailed causal certificates—such as source evidence, timing, and proposed causal roles—to AI‑derived features extracted from electronic health records (EHRs). These certificates provide auditable evidence for causal roles and guide whether a feature can be used for causal inference or should be routed to compatible reporting or separate analyses. The framework integrates with a joint EHR observation map and offers identification, estimation, and diagnostic tools under standard causal assumptions, illustrated through synthetic simulations and a finite‑world example.
By Takes Fujita (VRI), Nobutaka Hattori (Department of Neurology, Juntendo University School of Medicine)
arXiv:2604. 23904v3 Announce Type: replace-cross Abstract: Synthetic tabular data are often evaluated by distributional similarity, privacy distance, or train-on-synthetic-test-on-real predictive performance, but these criteria do not ensure validity for causal inference.
By Yichen Xu
The paper introduces the concept of causal retention in interactive agents, examining whether a frozen learned state can correctly answer a mechanism‑probe map that is fixed independently of training. It shows that for finite structural causal models the optimal probe error is a Bayes decision risk, vanishing only when each learning‑interface fiber lies within a single probe‑answer fiber, and provides theoretical results such as a posterior‑coverage theorem and an exact edit decomposition. Experiments on finite causal systems, continuous simulators, TD‑MPC2, and Qwen2.5‑7B‑Instruct demonstrate that causal retention can be achieved with high accuracy, outperforming task‑performance‑based approaches.
By Shengjun Zhang, Tingyi Liu, Dong Xie, Yunlong Dong, Xiang Wang, Cheng Zeng
arXiv:2608. 05235v1 Announce Type: cross Abstract: Research agents increasingly conduct multi-round machine-learning experiments in industrial recommendation settings and retain the resulting trajectories to guide later decisions.
By Zijie Zhuang, Changxin Lao, Pengbo Xu, Hanwen Xu, Ruochen Yang, Yingzhi He, Peng Zhang, Jiangxia Cao, Yusheng Huang, Guohong Mu, Jian Liang, Ruiming Tang, Shuang Yang, Zhaojie Liu, Wenwu Ou, Kun Gai
arXiv:2609.06294v1 Announce Type: new
Abstract: Estimating conditional average treatment effects (CATE) enables efficient targeting of interventions, but many applications have limited experimental s...
By Maitreyi Swaroop, Shikha Bhat, Samantha Rodriguez, Tamar Krishnamurti, Bryan Wilder
TabCausal is a causal discovery foundation model that learns to map datasets directly to causal graphs by pretraining across diverse causal environments. It uses a dynamic task construction strategy to expose the model to varied graph priors, mechanisms, noise models, dimensions, sample sizes, and intervention regimes, improving transferability from observational and mixed‑interventional data. On large synthetic benchmarks and a new protocol‑guided semantic benchmark, TabCausal outperforms many classical baselines and shows robust structure recovery, especially when interventional evidence is available.
By Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang, Han-Jia Ye
arXiv:2607. 08122v1 Announce Type: new Abstract: Workload-based differentially private (DP) synthetic data methods privately measure aggregate queries and post-process the noisy answers into synthetic records.
By Amir Asiaee, Kaveh Aryan
The paper studies when joint-embedding predictive architectures (JEPAs) can recover underlying causal states from high‑dimensional observations. It introduces a latent variable model where observations arise from causal states with action‑conditioned dynamics, and proposes an information‑theoretic objective that maximizes conditional likelihood while preserving state entropy. The authors prove identifiability conditions—particularly sufficient action‑induced variation—and instantiate the objective as an action‑modulated Gaussian additive‑noise model (A‑JEPA), demonstrating theoretical and empirical success in synthetic and visual benchmarks.
By Yuhang Liu, Zhuo Huang, Javen Qinfeng Shi
arXiv:2609.14124v1 Announce Type: cross
Abstract: Medical image analysis is often hindered by biased datasets, which can lead to biased models and limited clinical applicability. A promising strategy...
By Yasin Ibrahim, Robin J. Evans, Konstantinos Kamnitsas
arXiv:2607. 08349v1 Announce Type: new Abstract: Mechanistic interpretability often evaluates explanations by intervening on a model: swapping hidden states, patching activations, ablating components, or comparing a compressed model to the original one.
By Amir Asiaee
arXiv:2606. 21806v2 Announce Type: replace Abstract: Deep generative models reproduce the observational distribution of their training data, inheriting any spurious associations it contains.
By Jingyuan Chen, Kangrui Ruan, Junzhe Zhang