Evidence map›Paper›PMID 38833466›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2024

The origins of unpredictability in life outcome prediction tasks.

Ian Lundberg, Rachel Brown-Weinstock, Susan Clampet-Lundquist, Sarah Pachman, Timothy J Nelson, Vicki Yang, Kathryn Edin, Matthew J Salganik

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Evaluating the statistical realism of LLM-generated social science data.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. The value of qualitative data in understanding failure in prediction.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Ian LundbergDepartment of Information Science, Cornell University, Ithaca, NY 14853.ORCID 0000-0002-1909-2270
Rachel Brown-WeinstockDepartment of Sociology, Princeton University, Princeton, NJ 08544.
Susan Clampet-LundquistDepartment of Sociology, St. Joseph's University, Philadelphia, PA 19131.
Sarah PachmanOffice of Population Research, Princeton University, Princeton, NJ 08544.
Timothy J NelsonDepartment of Sociology, Princeton University, Princeton, NJ 08544.
Vicki YangDepartment of Sociology, Princeton University, Princeton, NJ 08544.
Kathryn EdinDepartment of Sociology, Princeton University, Princeton, NJ 08544.
Matthew J SalganikDepartment of Sociology, Princeton University, Princeton, NJ 08544.

Funding

Fragile Families and the Transition to AdulthoodR01HD036916 · NICHD · PRINCETON UNIVERSITY · PI Kathryn Edin, Jane Waldfogel · 1999 to 2026
$46.3M
Infrastructure for Population Research at PrincetonR24HD047879 · NICHD · PRINCETON UNIVERSITY · PI MASSEY, DOUGLAS S · 2004 to 2013
$7.3M
ECONOMIC STATUS, PUBLIC POLICY, AND CHILD NEGLECTR01HD039135 · NICHD · PRINCETON UNIVERSITY · PI PAXSON, CHRISTINA H · 2000 to 2004
$3.1M
Child Care and Parental Employment in Fragile FamiliesR01HD040421 · NICHD · COLUMBIA UNIVERSITY TEACHERS COLLEGE · PI BROOKS-GUNN, JEANNE · 2002 to 2006
$2.8M
Scientific and Technical Computing CoreP2CHD047879 · NICHD · PRINCETON UNIVERSITY · PI SALGANIK, MATTHEW J. · 2014 to 2018
$2.0M
HHS | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) P2-CHD047879National Science Foundation (NSF) 1760052NICHD NIH HHS R01 HD036916NICHD NIH HHS R01 HD039135NICHD NIH HHS R01 HD040421NICHD NIH HHS R01-HD36916 R01-HD39135 R01-HD40421NICHD NIH HHS R24 HD047879Overdeck Foundation, Princeton University The Fragile Families Challenge: Machine learning qualitative interviews and causal inference
6 · The paper itself

Abstract

Why are some life outcomes difficult to predict? We investigated this question through in-depth qualitative interviews with 40 families sampled from a multidecade longitudinal study. Our sampling and interviewing process was informed by the earlier efforts of hundreds of researchers to predict life outcomes for participants in this study. The qualitative evidence we uncovered in these interviews combined with a mathematical decomposition of prediction error led us to create a conceptual framework. Our specific evidence and our more general framework suggest that unpredictability should be expected in many life outcome prediction tasks, even in the presence of complex algorithms and large datasets. Our work provides a foundation for future empirical and theoretical work on unpredictability in human lives.

Indexed as

AlgorithmsAdultFemaleHumansLongitudinal StudiesMaleUncertaintylife courselimits to predictionmachine learningmixed methodsprediction

Identifiers

PMID38833466
PMCPMC11181083

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.