Causal inference methodologist studying AI-generated covariates in health data; early-stage OpenAlex-resolved profile
2
papers here
2
reviewed by Evolve
2
first-author
0
corresponding
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works · OpenAlex
0
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-01) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
Not yet scored: OpenAlex citation-linked records for this identity are insufficient. This axis would measure total works, recent output, and productivity trends; h-index and i10-index currently register 0 pending better name disambiguation.
Impact—
Not yet scored: the snapshot provides no usable citation counts, so influence cannot be assessed. This axis would measure citations, citing works, and uptake of the 2026 methodology preprints once citation linkage improves.
Focus0/100
Scores 0 of 100. The recorded field distribution splits evenly — 50% 'causal inference (statistics / biostatistics methodology)' and 50% 'biostatistics / causal inference methodology' — two near-duplicate labels that prevent a single dominant field. Topic counts nonetheless cluster tightly: Advanced Causal Inference Techniques, XAI, and Machine Learning in Healthcare (2 each), plus EHR Systems and Statistical Methods in Clinical Trials (1 each).
Collaboration28/100
Scores 28 of 100. The OpenAlex window records only 3 distinct co-authors, and the frequent-coauthor list names Nobutaka Hattori (repeated) alongside the author's own name — a likely record artifact. Both cataloged 2026 papers are first-authored with no corresponding-author role flagged, indicating a small, early collaboration network centered on methodological work rather than large consortium studies.
Activity57/100
Scores 57 of 100. The counts-by-year series shows 1 indexed work in 2014 and 6 in 2026, matching the 6 works in the recent three-year window — a burst of activity after a long thin stretch. The visible 2026 output includes two first-author arXiv preprints in causal-inference methodology, suggesting an active but recently re-established publication rhythm concentrated in a single year.
Reproducibility78/100
Scores 78 of 100, computed from 2 assessed papers. Evolve rates 'When AI Generates Covariates' as high reproducibility readiness and 'Information Set Emulation' as medium. Both verdicts praise methodological rigor yet flag that evidence is simulation-only or synthetic — one hinges on unverifiable type audits, the other on unproven feasibility — so the score reflects accessible, well-specified methods rather than validated real-data results.
Research summary
Takes Fujita, listed at Vejchewin Research Institute (country: IN), is a name-resolved OpenAlex profile of an early-stage researcher working at the intersection of causal inference methodology and AI-generated health data. The bibliometric snapshot is sparse: h-index and i10-index both register 0, the counts-by-year series shows one indexed work in 2014 followed by 6 works in 2026, and the output and impact axes are flagged as lacking sufficient citation-linked data. The evidence base rests on two first-authored 2026 arXiv preprints reviewed by Evolve. 'When AI Generates Covariates: Causal Typing and Estimand Drift in Sequential Experiments' proposes causal typing and estimand locks for sequential experiments and micro-randomized trials; Evolve judges it rigorous and honest but simulation-only, with high reproducibility readiness. 'Information Set Emulation: Causal Certificates for AI Derived EHR Features' introduces typed certificates gating LLM-extracted EHR covariates for target trial emulation and partial identification; its classical theory is rated sound, but all evidence is synthetic (medium readiness). Together these yield a reproducibility mean of 78 across 2 assessed papers. Topic counts — Advanced Causal Inference Techniques (2), Explainable AI (2), Machine Learning in Healthcare (2), EHR Systems (1), Statistical Methods in Clinical Trials (1) — and an even 50/50 field split between two adjacent biostatistics-methodology labels describe a tightly focused but narrowly documented agenda. Collaboration is limited (28/100; 3 distinct co-authors, including Nobutaka Hattori).
Collaboration pattern
The collaboration score is 28/100, with only 3 distinct co-authors recorded in the OpenAlex window. The frequent-coauthor list names Nobutaka Hattori twice and also includes the author's own name — a likely record artifact. Both cataloged papers are first-authored with no corresponding-author role, consistent with a small, early-stage network built around methodological work rather than large clinical consortia.
Research keywords
Highlights
Causal typing and estimand locks for AI-generated covariates
Typed causal certificates for LLM-extracted EHR features
A research program guarding causal inference against AI-generated data
Read across both 2026 papers and the topic counts (Advanced Causal Inference Techniques, XAI, and Machine Learning in Healthcare at 2 each), one consistent agenda emerges: certifying and typing AI-derived covariates so causal claims in healthcare survive automation. The field labels still split 50/50 between two adjacent biostatistics-methodology categories.
Timeline
- 2014OpenAlex counts-by-year records 1 indexed work for this author identity; no cataloged papers exist from this period, leaving the early record thinly documented.
- 2026Counts-by-year rises to 6 works, matching the 6 works recorded in the recent three-year window — the visible peak of documented activity.
- 2026Publishes first-authored arXiv paper 'When AI Generates Covariates: Causal Typing and Estimand Drift in Sequential Experiments' (reproducibility readiness: high).
- 2026Publishes first-authored arXiv paper 'Information Set Emulation: Causal Certificates for AI Derived EHR Features' (reproducibility readiness: medium).
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- Bibliometrics come from the OpenAlex identity snapshot only; no ORCID is provided, so name-disambiguation risk remains for this name.
- h-index and i10-index register 0, and the output and impact axes are unscored because citation-linked data is insufficient.
- The pre-2026 record is thin (one indexed work in 2014); the profile rests mainly on two 2026 arXiv preprints not shown as peer-reviewed.
- Evolve verdicts note simulation-only or synthetic evidence for both papers; the reproducibility score (78) covers only 2 assessed papers.