Evidence map›Paper›PMID 41394884›Full record

ArticleJournal of machine learning research : JMLR2025

Directed Cyclic Graphs for Simultaneous Discovery of Time-Lagged and Instantaneous Causality from Longitudinal Data Using Instrumental Variables.

Wei Jin, Yang Ni, Amanda B Spence, Leah H Rubin, Yanxun Xu

Abstract read
PubMed Full text at PMC
In one paragraph

Article in Journal of machine learning research : JMLR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Wei JinDepartment of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA.
Yang NiDepartment of Statistics, Texas A&M University, College Station, TX 77843, USA.
Amanda B SpenceDepartment of Medicine, Georgetown University, Washington, DC 20007, USA.
Leah H RubinDepartments of Neurology and Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
Yanxun XuDepartment of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA.

Funding

Bayesian Methods for Optimizing Combination Antiretroviral Therapy for Mentalhealth in People with HIVR01MH128085 · NIMH · JOHNS HOPKINS UNIVERSITY · PI Yanxun Xu · 2022 to 2026
$2.0M
Bayesian Differential Causal Network and Clustering Methods for Single-Cell Data (R01GM148974)R01GM148974 · NIGMS · UNIVERSITY OF TEXAS AT AUSTIN · PI NI, YANG · 2022 to 2025
$1.2M
SCH: New Bayesian Causal Structural Discovery and Reinforcement Learning Methods for Mental health in HIV CareR01AI197147 · NIAID · JOHNS HOPKINS UNIVERSITY · PI Yang Ni, Leah Helane Rubin · 2025 to 2026
$558k
NIAID NIH HHS R01 AI197147NIGMS NIH HHS R01 GM148974NIMH NIH HHS R01 MH128085
6 · The paper itself

Abstract

We consider the problem of causal discovery from longitudinal observational data. We develop a novel framework that simultaneously discovers the time-lagged causality and the possibly cyclic instantaneous causality. Under common causal discovery assumptions, combined with additional instrumental information typically available in longitudinal data, we prove the proposed model is generally identifiable. To the best of our knowledge, this is the first causal identification theory for directed graphs with general cyclic patterns that achieves unique causal identifiability. Structural learning is carried out in a fully Bayesian fashion. Through extensive simulations and an application to the Women's Interagency HIV Study, we demonstrate the identifiability, utility, and superiority of the proposed model against state-of-the-art alternative methods.

Indexed as

Bayesian structural learningCausal discoveryDirected cyclic graphInstrumental variableLongitudinal cohort study

Identifiers

PMID41394884
PMCPMC12700356

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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.