Evidence map›Paper›PMID 42754695›Full record

ReviewNature human behaviour2026

The future of large-scale experiments and their challenges in the digital era.

David Holtz, Iavor Bojinov, Ramesh Johari, Nathan Kallus, Apoorva Lal, Sathya Anand, Kyle Carlson, Brent Cohn, Tom Cunningham, Alex Deng and 18 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature human behaviour, 2026. 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

28 authors.

David HoltzDecision, Risk and Operations, Columbia Business School, New York, NY, USA. david.holtz@columbia.edu.ORCID http://orcid.org/0000-0002-0896-8628
Iavor BojinovTechnology & Operations Management, Harvard Business School, Boston, MA, USA.ORCID http://orcid.org/0000-0002-3470-8539
Ramesh JohariManagement Science & Engineering, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-3960-0770
Nathan KallusNetflix, Los Gatos, CA, USA.ORCID http://orcid.org/0000-0003-1672-0507
Apoorva LalOpenAI, San Francisco, CA, USA.
Sathya AnandNetflix, Los Gatos, CA, USA.
Kyle CarlsonIndependent researcher, San Francisco, CA, USA.
Brent CohnStripe, San Francisco, CA, USA.
Tom CunninghamMETR, Berkeley, CA, USA.
Alex DengIndependent researcher, Bellevue, WA, USA.
Maria DimakopoulouUber, San Francisco, CA, USA.
Amit GandhiEconomics, Wharton School of the University of Pennsylvania, Philadelphia, PA, USA.
Victor KostyukBraze, Cambridge, MA, USA.
Madhav KumarMarketing, Harvard Business School, Boston, MA, USA.
Shyue-Ming LohIndependent researcher, Daly City, CA, USA.
Widad MachmouchiMicrosoft, Redmond, WA, USA.
Jialiang MaoUber, San Francisco, CA, USA.
James McQueenAmazon, Seattle, WA, USA.
John MeakinMeta, Menlo Park, CA, USA.
Konrad MiziolekIndependent researcher, San Francisco, CA, USA.
Dominique Perrault-JoncasAmazon, Seattle, WA, USA.
Vladimir PetrovicMeta, Menlo Park, CA, USA.
Sven SchmitOpenAI, San Francisco, CA, USA.
James SorensonIndependent researcher, Stonington, CT, USA.
Matthew WardropNetflix, Los Gatos, CA, USA.
Michael ZhaoDoorDash, San Francisco, CA, USA.
Wenjing ZhengRoblox, San Mateo, CA, USA.
Martin TingleyIndependent researcher, Boulder, CO, USA. martin.tingley@gmail.com.ORCID http://orcid.org/0009-0003-6204-0235

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Large-scale randomized experiments involving millions of observations have transformed social science across academic and applied domains. These methods are now widely used in fields ranging from economics and political science to public health, education and digital product development. Drawing on the experience of industry practitioners and academic collaborators, this Perspective outlines six areas where the expanding scale and scope of experimental studies have introduced new challenges. We describe open problems related to organizational incentives and experimental governance; privacy, fairness and ethics; long-term impact estimation; time-adaptive experimental studies; heterogeneous treatment effects; and generative artificial intelligence. For each area, we summarize recent methodological advances, highlight ongoing limitations and suggest directions for future work. Our goal is to surface practical challenges that merit greater attention from the research community and to encourage sustained collaboration between academics and practitioners in shaping the future of experimental studies.

Identifiers

PMID42754695

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.