RRepoGEO

REPOGEO REPORT · LITE

causaltext/causal-text-papers

Default branch master · commit af25a39a · scanned 6/4/2026, 3:07:49 PM

GitHub: 815 stars · 102 forks

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 causaltext/causal-text-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
  • highreadme#1
    Reposition the README's opening to emphasize 'curated collection'

    Why:

    CURRENT
    A collection of papers and codebases about influence, causality, and language.
    COPY-PASTE FIX
    A curated collection of research papers and codebases at the intersection of causal inference and natural language processing.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Add a LICENSE file (e.g., MIT, Apache-2.0, or CC-BY-4.0 for content) to the repository root.
  • mediumtopics#3
    Expand repository topics for better categorization

    Why:

    CURRENT
    causality, natural-language-processing
    COPY-PASTE FIX
    causality, natural-language-processing, nlp, causal-inference, research-papers, curated-list, literature-review

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 causaltext/causal-text-papers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
arXiv.org
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. arXiv.org · recommended 1×
  2. ACL Anthology · recommended 1×
  3. Google Scholar · recommended 1×
  4. NeurIPS · recommended 1×
  5. ICML · recommended 1×
  • CATEGORY QUERY
    Where can I find academic papers on causal inference applied to natural language processing tasks?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. ACL Anthology
    3. Google Scholar
    4. NeurIPS
    5. ICML
    6. ICLR
    7. Journal of Machine Learning Research (JMLR)
    8. Conference on Causal Learning and Reasoning - CLeaR

    AI recommended 8 alternatives but never named causaltext/causal-text-papers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking research on how text data can act as a treatment or outcome in causal models.
    you: not recommended
    AI recommended (in order):
    1. Text-based Propensity Score Matching (TPSM)
    2. Targeted Maximum Likelihood Estimation (TMLE)
    3. Augmented Inverse Probability Weighting (AIPW)
    4. Text-based Instrumental Variables (TIV)
    5. Synthetic Control Methods
    6. Causal Mediation Analysis
    7. Generalized Linear Models (GLMs)
    8. LDA
    9. NMF
    10. Procrustes Analysis
    11. Granger Causality
    12. PC Algorithm (Peter-Clark Algorithm)
    13. FCI Algorithm (Fast Causal Inference)
    14. TF-IDF
    15. Word2Vec
    16. Doc2Vec
    17. BERT embeddings
    18. VADER
    19. TextBlob
    20. PCA
    21. UMAP
    22. Lasso
    23. Ridge

    AI recommended 23 alternatives but never named causaltext/causal-text-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
    warn

    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 causaltext/causal-text-papers?
    pass
    AI named causaltext/causal-text-papers explicitly

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

  • If a team adopts causaltext/causal-text-papers in production, what risks or prerequisites should they evaluate first?
    pass
    AI named causaltext/causal-text-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 causaltext/causal-text-papers solve, and who is the primary audience?
    pass
    AI did not name causaltext/causal-text-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

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