HIGHLY ADAPTIVE LASSO: MACHINE LEARNING THAT PROVIDES VALID NONPARAMETRIC INFERENCE IN REALISTIC MODELS.

Zachary Butzin-Dozier, Sky Qiu, Alan E Hubbard, Junming Seraphina Shi, Mark J van der Laan

Abstract readPreprint

In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. 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

5 authors.

Zachary Butzin-DozierDepartment of Biostatistics, University of California, Berkeley, Berkeley, CA 94704.ORCID 0000-0001-6419-0008

Sky QiuDepartment of Biostatistics, University of California, Berkeley, Berkeley, CA 94704.ORCID 0009-0006-5944-0782

Alan E HubbardDepartment of Biostatistics, University of California, Berkeley, Berkeley, CA 94704.ORCID 0000-0002-3769-0127

Junming Seraphina ShiDepartment of Biostatistics, University of California, Berkeley, Berkeley, CA 94704.ORCID 0000-0002-1520-8387

Mark J van der LaanDepartment of Biostatistics, University of California, Berkeley, Berkeley, CA 94704.ORCID 0000-0003-1432-5511

Funding

Applying a Targeted Machine Learning and Causal Inference Approach to Analyzing Long-Term Sequelae of COVID-19 Infection Through the National COVID Cohort Collaborative.K01AI182501 · NIAID · UNIVERSITY OF CALIFORNIA BERKELEY · PI Zachary Butzin-Dozier · 2024 to 2026

$326k

NIAID NIH HHS K01 AI182501

6 · The paper itself

Abstract

Understanding treatment effects on health-related outcomes using real-world data requires defining a causal parameter and imposing relevant identification assumptions to translate it into a statistical estimand. Semiparametric methods, like the targeted maximum likelihood estimator (TMLE), have been developed to construct asymptotically linear estimators of these parameters. To further establish the asymptotic efficiency of these estimators, two conditions must be met: 1) the relevant components of the data likelihood must fall within a Donsker class, and 2) the estimates of nuisance parameters must converge to their true values at a rate faster than

Indexed as

Causal InferenceMachine LearningTargeted Learning

Identifiers

PMID39484253

DOI10.1101/2024.10.18.24315778

PMCPMC11527044

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.