arXiv:2608. 00657v1 Announce Type: cross Abstract: Causal inference usually concerns a scalar treatment, yet in many problems the treatment is unstructured: a text, an image, or a sequence of clinical decisions.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2608. 03085v1 Announce Type: cross Abstract: Causal inference has traditionally centered on scalar outcomes: whether a patient recovers, how much a worker earns, or how many visits a website receives.
arXiv:2606. 23741v1 Announce Type: cross Abstract: Causal reasoning, which encompasses the discovery of causal structures and the inference of causal effects, is fundamental to data-driven decision making.
arXiv:2605. 15133v2 Announce Type: replace Abstract: Causal inference, estimating causal effects from observational data, is a fundamental tool in many disciplines.
arXiv:2607. 11508v1 Announce Type: cross Abstract: Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines.
arXiv:2507. 20993v4 Announce Type: replace-cross Abstract: We study how to learn treatment policies from multimodal electronic health records (EHRs) that consist of tabular data and clinical text.
arXiv:2606. 18933v1 Announce Type: new Abstract: Active feature acquisition (AFA) sequentially selects which features to observe to reach a classification or ranking decision.