Evidence map›Paper›PMID 37416628›Full record

ArticleBiometrika2022

Fast and powerful conditional randomization testing via distillation.

Molei Liu, Eugene Katsevich, Lucas Janson, Aaditya Ramdas

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biometrika, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. AICO: Feature significance tests for supervised learning.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Article
  3. Microbiome mediation analysis: methods, assumptions, and practical considerations.Frontiers in cellular and infection microbiology · 2026
    Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Contra: Contrarian statistics for controlled variable selection.Proceedings of machine learning research · 2021
    Article
  11. Double Empirical Bayes Testing.International statistical review = Revue internationale de statistique · 2020
    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

4 authors.

Molei LiuDepartment of Biostatistics, Harvard Chan School of Public Health, 677 Huntington Avenue, Boston, Massachusetts 02115, U.S.A.
Eugene KatsevichDepartment of Statistics and Data Science, Wharton School of the University of Pennsylvania, 265 South 37th Street, Philadelphia, Pennsylvania 19104, U.S.A.
Lucas JansonDepartment of Statistics, Harvard University, One Oxford Street, Cambridge, Massachusetts 02138, U.S.A.
Aaditya RamdasDepartment of Statistics & Data Science, Carnegie Mellon University, 132H Baker Hall, Pittsburgh, Pennsylvania 15213, U.S.A.

Funding

Semi-supervised Approaches to Denoising Electronic Health Records Data for Risk PredictionR01LM013614 · NLM · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI CAI, TIANXI, GUO, ZIJIAN · 2021 to 2024
$1.4M
NLM NIH HHS R01 LM013614
6 · The paper itself

Abstract

We consider the problem of conditional independence testing: given a response

Indexed as

Conditional independence testConditional randomization testHigh-dimensional inferenceMachine learningModel-X

Identifiers

PMID37416628
PMCPMC10323874

What OpenQuestion holds

Textmetadata
LicenceTDM
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.