View PDF ↗
PDF Viewer
Open Full PDF

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (33)

[1]
Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
2025Haoang Chi, He Li et al.
[2]
Causality for Large Language Models
2024Anpeng Wu, Kun Kuang et al.
[3]
Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answering
2024Zimu Wang, Lei Xia et al.
[4]
Causal Inference with Large Language Model: A Survey
2024Jing Ma
[5]
Causality extraction from medical text using Large Language Models (LLMs)
2024Seethalakshmi Gopalakrishnan, Luciana D. Garbayo et al.
[6]
Cause and Effect: Can Large Language Models Truly Understand Causality?
2024Swagata Ashwani, Kshiteesh Hegde et al.
[7]
Is ChatGPT the Future of Causal Text Mining? A Comprehensive Evaluation and Analysis
2024Takehiro Takayanagi, Masahiro Suzuki et al.
[8]
Large Language Models for Causal Discovery: Current Landscape and Future Directions
2024Guangya Wan, Yunsheng Lu et al.
[9]
CausalBench: A Comprehensive Benchmark for Evaluating Causal Reasoning Capabilities of Large Language Models
2024Zeyu Wang
[10]
Explicit and implicit knowledge-enhanced model for event causality identification
2024Siyuan Chen, Kezhi Mao
[11]
BoschAI @ Causal News Corpus 2023: Robust Cause-Effect Span Extraction using Multi-Layer Sequence Tagging and Data Augmentation
2023Timo Pierre Schrader, S. Razniewski et al.
[12]
Event Causality Is Key to Computational Story Understanding
2023Yidan Sun, Qin Chao et al.
[13]
Text to Causal Knowledge Graph: A Framework to Synthesize Knowledge from Unstructured Business Texts into Causal Graphs
2023Seethalakshmi Gopalakrishnan, Victor Zitian Chen et al.
[14]
Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
2023Emre Kıcıman, R. Ness et al.
[15]
Event Causality Extraction via Implicit Cause-Effect Interactions
2023Jintao Liu, Zequn Zhang et al.
[16]
From Query Tools to Causal Architects: Harnessing Large Language Models for Advanced Causal Discovery from Data
2023Taiyu Ban, Lyuzhou Chen et al.
[17]
MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction
2022Xiaozhi Wang, Yulin Chen et al.
[18]
Extraction of Explicit and Implicit Cause-Effect Relationships in Patient-Reported Diabetes-Related Tweets From 2017 to 2021: Deep Learning Approach
2022Adrian Ahne, Vivek Khetan et al.
[19]
Mining Health-related Cause-Effect Statements with High Precision at Large Scale
2022Ferdinand Schlatt, D. Bettin et al.
[20]
Investigating causal understanding in LLMs
2022Marius Hobbhahn, Tom Lieberum et al.

Showing 20 of 33 references

Founder's Pitch

"Benchmark platform for testing LLMs on causal discovery from text in biomedical and diverse contexts."

BenchmarkingScore: 5View PDF ↗

Commercial Viability Breakdown

Breakdown pending for this paper.

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 1/21/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.