Evidence map›Paper›PMID 42866051›Full record

ArticleProceedings of machine learning research2026

Smooth Multi-Policy Causal Effect Estimation in Longitudinal Settings.

Wenxin Chen, Weishen Pan, Kyra Gan, Fei Wang

Abstract read
In one paragraph

Article in Proceedings of machine learning research, 2026. 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

4 authors.

Wenxin ChenCornell University, New York, US.
Weishen PanCornell University, New York, US.
Kyra GanCornell University, New York, US.
Fei WangCornell University, New York, US.

Funding

TREM2 Genotype-Informed Drug Repurposing and Combination Therapy Design for Alzheimer’s DiseaseR01AG076448 · NIA · CLEVELAND CLINIC LERNER COM-CWRU · PI Feixiong Cheng, Li Gan · 2022 to 2026
$4.0M
Computational Drug Repurposing for AD/ADRD with Integrative Analysis of Real World Data and Biomedical KnowledgeR01AG076234 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI Jiang Bian, Fei Wang · 2022 to 2026
$3.8M
Eligibility criteria design for Alzheimer's trials with real-world data and explainable AIR01AG080991 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI Jiang Bian, Fei Wang · 2023 to 2026
$3.1M
Disparities of Alzheimer's disease progression in Sexual Minority IndividualsR01AG080624 · NIA · UNIVERSITY OF FLORIDA · PI Jiang Bian, Yi Guo · 2023 to 2026
$3.1M
Identification of Mild Cognitive Impairment using Machine Learning from Language and Behavior MarkersRF1AG072449 · NIA · MICHIGAN STATE UNIVERSITY · PI DODGE, HIROKO HAYAMA, WANG, FEI · 2021 to 2023
$2.6M
Post-Acute Sequelae of SARS-CoV-2 Infection and Subsequent Disease Progression in Individuals with AD/ADRD: Influence of the Social and Environmental Determinants of HealthRF1AG084178 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI BIAN, JIANG, HU, HUI · 2023 to 2023
$2.6M
Identification of Mild Cognitive Impairment using Machine Learning from Language and Behavior MarkersR01AG072449 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HIROKO Hayama DODGE, Fei Wang · 2025 to 2026
$1.4M
Progression Subtyping and Drug Target Identification for Parkinson's Disease with Integrative Machine LearningR01NS140142 · NINDS · WEILL MEDICAL COLL OF CORNELL UNIV · PI Chang Su · 2025 to 2026
$1.3M
NIA NIH HHS R01 AG072449NIA NIH HHS R01 AG076234NIA NIH HHS R01 AG076448NIA NIH HHS R01 AG080624NIA NIH HHS R01 AG080991NIA NIH HHS RF1 AG072449NIA NIH HHS RF1 AG084178NINDS NIH HHS R01 NS140142
6 · The paper itself

Abstract

Comparative evaluation of

Identifiers

PMID42866051
PMCPMC13655700

What Socratic 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 Socratic graph.