Causal ML researcher at Universitat de València applying double machine learning to climate and food-security questions
2
papers here
2
reviewed by Evolve
2
first-author
0
corresponding
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works · OpenAlex
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h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-13) 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—
No output metrics are available yet; the evidence base is just two 2026 arXiv preprints. This axis would measure total and annualized publication counts, venue types, and authorship positions across the full bibliographic record.
Impact—
No citation counts, h-index, or downstream-uptake indicators are available for this profile. This axis would measure citations, altmetrics, and influence on the food-security and causal-ML literature once the bibliographic record accumulates.
Focus0/100
The radar scores focus at 0 in this snapshot. The raw field distribution splits evenly (0.5/0.5) between two adjacent labels — causal machine learning for climate and food security, and causal machine learning for food security/humanitarian research — so both cataloged papers sit in one narrow problem area: estimating causal effects on food insecurity. With only two papers, the even split reflects sample size more than demonstrated breadth, so this score should be read cautiously.
Collaboration44/100
Collaboration scores 44, based on 8 distinct coauthors recorded across the cataloged work. Frequent coauthors include Gustau Camps-Valls, Vasileios Sitokonstantinou, Gherardo Varando, Michele Ronco, Homer Durand, Duccio Piovani, José Manuel Veiga López-Peña, and José María Tárraga. Both papers are first-authored but not corresponding, a pattern consistent with leading the analysis within a senior-supervised team; the moderate score reflects a small, concentrated coauthor pool at this career stage.
Activity—
No year-by-year publication counts are available, so activity is unscored. This axis would measure annual output cadence and recent productivity trends; the catalog's two papers both date to 2026, too few data points to establish a trajectory.
Reproducibility55/100
Reproducibility scores 55, the mean readiness rating across two Evolve-reviewed papers (reproPapers = 2), each rated medium. For the ENSO and price-spike study, the review credited a sound double machine learning design but noted the key regime contrast is only marginally significant; for the Somalia credit-access study, transparent framing was praised while identification was called thin and several refutation tests failed. Method-level caveats in both papers would be clarified by reproducible code and data releases.
Research summary
Jordi Cerdà-Bautista is a researcher at the Universitat de València working at the intersection of causal machine learning and food-security research. The current Evolve catalog contains two first-authored arXiv preprints, both dated 2026, which together define a tightly scoped research line: estimating how climatic and economic conditions causally affect food insecurity in vulnerable regions. The first paper uses double machine learning to argue that ENSO-driven climate variability modulates the impact of price spikes on food insecurity in Africa; Evolve's review found the DML design sound while noting that the key regime contrast is only marginally significant. The second applies causal inference with an X-Learner to estimate that credit access is associated with roughly a 2% reduction in food insecurity in Somalia; the review praised transparent framing but flagged thin identification and several failed refutation tests. Bibliometrically the profile is early-stage: eight distinct coauthors are recorded, with collaboration centered on a recurring coauthor set including Gustau Camps-Valls, Vasileios Sitokonstantinou, and Gherardo Varando. The radar scores collaboration at 44 and reproducibility at 55 (the mean over two papers rated medium), while the output, impact, and activity axes lack sufficient data to score. Two first-authored arXiv releases in a single year suggest an active, method-driven agenda whose evidential weight should grow with peer-reviewed validation and reproducible releases.
Collaboration pattern
Both cataloged papers are first-authored but not corresponding, consistent with leading analyses within a senior-supervised team. Eight distinct coauthors are recorded (radar collaboration score: 44); the frequent-coauthor list comprises Gustau Camps-Valls, Vasileios Sitokonstantinou, Gherardo Varando, Michele Ronco, Homer Durand, Duccio Piovani, José Manuel Veiga López-Peña, and José María Tárraga. The pattern suggests a stable single-team collaboration cluster rather than distributed multi-institution networks, though coauthor affiliations are not in the available data.
Research keywords
Highlights
ENSO regimes as moderators of price-spike shocks on African food insecurity
Estimating the food-security effect of credit access in Somalia with an X-Learner
A deliberately scoped causal-ML-for-food-security agenda within one year
Both 2026 catalog entries are first-authored and target the same problem class — causal estimation of food-insecurity drivers — one centered on climate modulation (ENSO and price spikes) and one on economic access (credit in Somalia), indicating a coherent early agenda rather than scattered topics; both are arXiv-stage with medium reproducibility ratings.
Timeline
- 2026Releases the first cataloged preprint, 'Climate Variability Modulates the Impact of Price Spikes on Food Insecurity', as first author on arXiv, applying double machine learning to ENSO-by-price-spike interaction effects in Africa.
- 2026Releases 'Credit Access is Associated with Improved Food Security in the Horn of Africa' as first author, extending the causal machine learning toolkit (X-Learner, average treatment effect) to credit access and IPC-classified food insecurity in Somalia.
- 2026Both entries are reviewed in the Evolve catalog with medium reproducibility ratings, establishing an early-stage profile centered on causal machine learning for food security at the Universitat de València.
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.
- Radar scores and bibliometric raw values derive from a limited OpenAlex-based snapshot covering only the two 2026 arXiv preprints in the Evolve catalog.
- Output, impact, and activity axes are unscored: no citation counts, publication totals, or year-by-year output data were available.
- Reproducibility (55) is the mean over just two reviewed papers, both rated medium; the Evolve verdicts note marginal significance in one study and several failed refutation tests in the other.
- Profile is name-resolved from public bibliographic data (ORCID 0000-0003-4512-6005); country and coauthor affiliations are not in the input data.