Methodological and regulatory considerations for causal AI in drug development.

Hana Lee, Sky Qiu, Spencer Haupert, Gabriel K Innes, Tristan Naumann, Demissie Alemayehu, Mark van der Laan

Abstract readReview

In one paragraph

Review in NPJ digital medicine, 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

7 authors.

Hana LeeCenter for Drug Evaluation and Research, U.S. Food and Drug Administration (FDA), Silver Spring, MD, USA. hana.lee@fda.hhs.gov.

Sky QiuDivision of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA.

Spencer HaupertCenter for Drug Evaluation and Research, U.S. Food and Drug Administration (FDA), Silver Spring, MD, USA.

Gabriel K InnesCenter for Drug Evaluation and Research, U.S. Food and Drug Administration (FDA), Silver Spring, MD, USA.

Tristan NaumannMicrosoft Research, Redmond, WA, USA.

Demissie AlemayehuPfizer, Inc., New York, NY, USA.

Mark van der LaanDivision of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advances in AI offer significant opportunities to enhance drug development. While several regulatory agencies have begun issuing guidance on AI adoption, its application to causal inference-a critical piece to understand treatment effects and inform regulatory decisions-remains limited. This paper reviews regulatory activities and examines statistical methodologies for AI-driven causal inference. We discuss key regulatory challenges and illustrate how AI adds value across diverse data sources and studies.

Identifiers

PMID41760772

DOI10.1038/s41746-026-02477-w

PMCPMC13065995

What OpenQuestion holds

Textmetadata

LicenceCC BY

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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.