Evidence map›Paper›PMID 38680250›Full record

ArticleJournal of econometrics2024

Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators.

Lin Liu, Rajarshi Mukherjee, James M Robins

Abstract read
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In one paragraph

Article in Journal of econometrics, 2024. 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

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

1 citing paper in PubMed.

  1. Article
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.

Lin LiuInstitute of Natural Sciences, MOE-LSC, School of Mathematical Sciences, CMA-Shanghai, SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University; Shanghai Artificial Intelligence Laboratory.
Rajarshi MukherjeeDepartment of Biostatistics, Harvard University.
James M RobinsDepartment of Epidemiology and Biostatistics, Harvard University.

Funding

Closing the gap between observational research and randomized trials for prevention of Alzheimer's Disease and dementiaR01AG057869 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GRAFF, REBECCA ELIZABETH, POWER, MELINDA C · 2018 to 2022
$3.9M
Next Generation Missing Data Methods in HIV ResearchR01AI127271 · NIAID · UNIVERSITY OF PENNSYLVANIA · PI TCHETGEN TCHETGEN, ERIC JOEL · 2017 to 2021
$3.2M
Novel Designs and Methods to Remove Hidden Confounding Bias in Health SciencesR01AG065276 · NIA · UNIVERSITY OF PENNSYLVANIA · PI TCHETGEN TCHETGEN, ERIC JOEL · 2020 to 2024
$2.4M
NIAID NIH HHS R01 AI127271NIA NIH HHS R01 AG057869NIA NIH HHS R01 AG065276
6 · The paper itself

Abstract

The class of doubly robust (DR) functionals studied by Rotnitzky et al. (2021) is of central importance in economics and biostatistics. It strictly includes both (i) the class of mean-square continuous functionals that can be written as an expectation of an affine functional of a conditional expectation studied by Chernozhukov et al. (2022b) and the class of functionals studied by Robins et al. (2008). The present state-of-the-art estimators for DR functionals

Indexed as

Doubly Robust FunctionalsEconometrics and Machine LearningHigher-Order U-Statistics

Identifiers

PMID38680250
PMCPMC11052545

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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.