RRepoGEO

REPOGEO REPORT · LITE

zhijing-jin/CausalNLP_Papers

Default branch main · commit 040f8e5d · scanned 6/14/2026, 6:32:11 PM

GitHub: 691 stars · 73 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface zhijing-jin/CausalNLP_Papers, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    ["causality", "nlp", "natural-language-processing", "causal-inference", "reading-list", "papers", "research", "interpretability", "robustness", "machine-learning"]
  • highlicense#2
    Add a LICENSE file to clarify usage rights

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) in the root of the repository.
  • mediumhomepage#3
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    Set the homepage URL to "https://github.com/zhijing-jin/CausalNLP_Papers" or a dedicated project page if one exists.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface zhijing-jin/CausalNLP_Papers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Causal Inference in Statistics: A Primer
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Causal Inference in Statistics: A Primer · recommended 1×
  2. Elements of Causal Inference: Foundations and Learning Algorithms · recommended 1×
  3. Causal Inference for The Brave and True · recommended 1×
  4. Causality: Models, Reasoning, and Inference · recommended 1×
  5. py-why/dowhy · recommended 1×
  • CATEGORY QUERY
    What are good resources for understanding causality applications in natural language processing?
    you: not recommended
    AI recommended (in order):
    1. Causal Inference in Statistics: A Primer
    2. Elements of Causal Inference: Foundations and Learning Algorithms
    3. Causal Inference for The Brave and True
    4. Causality: Models, Reasoning, and Inference
    5. DoWhy (py-why/dowhy)
    6. EconML (microsoft/EconML)

    AI recommended 6 alternatives but never named zhijing-jin/CausalNLP_Papers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find papers on causal methods for NLP interpretability and robustness?
    you: not recommended
    AI recommended (in order):
    1. ACL Anthology
    2. arXiv
    3. NeurIPS
    4. ICML
    5. ICLR
    6. EMNLP
    7. ACL
    8. NAACL
    9. Journal of Artificial Intelligence Research (JAIR)
    10. Transactions of the Association for Computational Linguistics (TACL)
    11. Google Scholar
    12. Semantic Scholar

    AI recommended 12 alternatives but never named zhijing-jin/CausalNLP_Papers. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of zhijing-jin/CausalNLP_Papers?
    pass
    AI did not name zhijing-jin/CausalNLP_Papers — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts zhijing-jin/CausalNLP_Papers in production, what risks or prerequisites should they evaluate first?
    pass
    AI named zhijing-jin/CausalNLP_Papers explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo zhijing-jin/CausalNLP_Papers solve, and who is the primary audience?
    pass
    AI did not name zhijing-jin/CausalNLP_Papers — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of zhijing-jin/CausalNLP_Papers. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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zhijing-jin/CausalNLP_Papers — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite