GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints.

Mohammadsajad Abavisani, David Danks, Sergey M Plis

Abstract read

In one paragraph

Article in ... International Conference on Learning Representations, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Mohammadsajad AbavisaniDepartment of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332.

David DanksHalicioglu Data Science Institute and Department of Philosophy, University of California San Diego, San Diego, CA 92093.

Sergey M PlisTReNDS center, Department of Computer Science, Georgia State University, Atlanta, GA 30302.

Funding

Multivariate methods for identifying multitask/multimodal brain imaging biomarkersR01EB006841 · NIBIB · THE MIND RESEARCH NETWORK · PI VINCE D CALHOUN, Sergey Plis · 2007 to 2026

$9.8M

CRCNS: Multimodal Dynamic Causal Learning for NeuroimagingR01MH129047 · NIMH · GEORGIA STATE UNIVERSITY · PI PLIS, SERGEY · 2021 to 2024

$1.3M

NIBIB NIH HHS R01 EB006841NIMH NIH HHS R01 MH129047

6 · The paper itself

Abstract

Graphical structures estimated by causal learning algorithms from time series data can provide misleading causal information if the causal timescale of the generating process fails to match the measurement timescale of the data. Existing algorithms provide limited resources to respond to this challenge, and so researchers must either use models that they know are likely misleading, or else forego causal learning entirely. Existing methods face up-to-four distinct shortfalls, as they might

Identifiers

PMID42775325

PMCPMC13596948

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.