Add study-design overview, headline findings, and analysis cookbook

README.md

@@ -41,6 +41,33 @@ Learning."** Designed for fast verification, not full reproduction.
 
 ---
 
+## What we did (1-paragraph)
+
+We ask which content features of a web page *cause* an LLM reranker
+to keep that URL in its top-K (admission) and to push it toward the
+top — separating cause from spurious correlation with confounders
+like domain authority. Six features are declared a priori as
+treatments (stats density, question-headings, JSON-LD schema,
+citation authority, topical competence, freshness) and tested with
+Double/Debiased ML controlling for 28 confounders plus the other
+five treatments mutually. 65K (keyword × url × condition) rows
+across 4 prompt variants × 2 reranker backbones (Llama-3.3-70B,
+Qwen-2.5-72B) × 2 SERP backends × 2 candidate-pool sizes feed 216
+fitted DML models. Mechanism is verified by three orthogonal
+interpretability probes (linear probes on hidden states, gradient
+saliency, token ablation).
+
+**Headline.** T5 topical competence is the dominant promoter; T3
+JSON-LD schema is a small *demoter* (i.e. it does **not** help —
+counter to common SEO advice); T1b stats density has high Qwen
+saliency (1.93×) but zero DML coefficient — *attention is not effect*.
+
+For deeper analysis (re-fitting DML on custom slices, inspecting raw
+LLM rerank outputs, plotting layer-wise probes), grab the full pack:
+[`ValerianFourel/geodml-emnlp-2026`](https://huggingface.co/datasets/ValerianFourel/geodml-emnlp-2026).
+
+---
+
 ## Setup (60 seconds)
 
 ```bash