Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2387873196
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Masahiro Kato (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Innovation: 1 paper
- Productivity: 1 paper
Claim outcomes
- Research Productivity: 1 paper
- Adoption Rate: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| A new Python package, genriesz, automates debiased machine-learning estimation of causal parameters using the Riesz representation and Bregman-divergence minimization; it delivers regression-adjustment, weighting, and TMLE-style estimators with cross-fitting, confidence intervals and flexible model bases. The tool unifies covariate balancing, matching, density-ratio estimation and other approaches, making principled causal estimation more accessible for applied researchers.arxiv | Masahiro Kato provider id |
2026-02-19 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.