Evidence map›Paper›PMID 34194044›Full record

ArticleNature2021

Integrating explanation and prediction in computational social science.

Jake M Hofman, Duncan J Watts, Susan Athey, Filiz Garip, Thomas L Griffiths, Jon Kleinberg, Helen Margetts, Sendhil Mullainathan, Matthew J Salganik, Simine Vazire and 2 more

Abstract read
PubMed Publisher
In one paragraph

Article in Nature, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.

0numbers the graph read from it
0cells of the map it votes in
69citing 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

69 citing papers in PubMed.

  1. Critical-Network Lesion-Symptom Mapping.Human brain mapping · 2026
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  12. Data-driven equation discovery reveals nonlinear reinforcement learning in humans.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
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  15. Review
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  19. Review
  20. Article

9 more citing papers are in PubMed but not listed here.

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

12 authors.

Jake M Hofman *Microsoft Research, New York, NY, USA. jmh@microsoft.com.ORCID http://orcid.org/0000-0002-9364-9604
Duncan J Watts *Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA. djwatts@seas.upenn.edu.
Susan AtheyGraduate School of Business, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-6934-562X
Filiz GaripDepartment of Sociology, Princeton University, Princeton, NJ, USA.
Thomas L GriffithsDepartment of Psychology, Princeton University, Princeton, NJ, USA.
Jon KleinbergDepartment of Computer Science, Cornell University, Ithaca, NY, USA.
Helen MargettsOxford Internet Institute, University of Oxford, Oxford, UK.
Sendhil MullainathanBooth School of Business, University of Chicago, Chicago, IL, USA.
Matthew J SalganikDepartment of Sociology, Princeton University, Princeton, NJ, USA.
Simine VazireMelbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia.
Alessandro VespignaniLaboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.ORCID http://orcid.org/0000-0003-3419-4205
Tal YarkoniDepartment of Psychology, University of Texas at Austin, Austin, TX, USA.ORCID http://orcid.org/0000-0002-6558-5113

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyse them. It also represents a convergence of different fields with different ways of thinking about and doing science. The goal of this Perspective is to provide some clarity around how these approaches differ from one another and to propose how they might be productively integrated. Towards this end we make two contributions. The first is a schema for thinking about research activities along two dimensions-the extent to which work is explanatory, focusing on identifying and estimating causal effects, and the degree of consideration given to testing predictions of outcomes-and how these two priorities can complement, rather than compete with, one another. Our second contribution is to advocate that computational social scientists devote more attention to combining prediction and explanation, which we call integrative modelling, and to outline some practical suggestions for realizing this goal.

Indexed as

Computer SimulationModels, TheoreticalData ScienceForecastingGoalsHumansSocial Sciences

Identifiers

What OpenQuestion 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 OpenQuestion graph.