Code and outputs: provenance-gated causally-informed XAI for crop yield prediction (T-PGCE)

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Analysis code, simulation code and reference outputs (main and supplementary tables and figures) accompanying the manuscript "A Provenance-Gated Framework for Causally-Informed Explainable AI in Crop Yield Prediction: Identification Diagnostics for Hypothesis-Generating Causal Explanation". The archive contains a single entry point (RUN_ALL.py), nine seeded analysis stages (preprocessing, causal discovery, predictive models, exposure effect, explanations, validation, rating analysis, semi-synthetic simulation, tables and figures), and supplementary checks (clustered missingness test, Moran's I). Third-party input data (CYCleSS, CropClimateX) are not redistributed; see README.md for sources. Anonymized expert ratings are available on reasonable request.