# IFAR: Multi-Perspective and Multi-Level Causal Discovery with LLMs

arXiv: https://arxiv.org/abs/2409.05559 · HTML: https://mlsanity.com/paper/2409.05559/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 24 of 24)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [Graph of Thoughts: Solving Elaborate Problems with Large Language Models](https://mlsanity.com/paper/2308.09687/) (2023)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [GPT Understands, Too](https://mlsanity.com/paper/2103.10385/) (2021)
- [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022)
- [ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language](https://mlsanity.com/paper/2012.13048/) (2020)
- [Abductive Commonsense Reasoning](https://mlsanity.com/paper/1908.05739/) (2019)
- [SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks](https://mlsanity.com/paper/2305.17390/) (2023)
- [XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters](https://mlsanity.com/paper/2305.12002/) (2023)
- [Integrating Large Language Model for Improved Causal Discovery](https://mlsanity.com/paper/2306.16902/) (2023)
- [ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing](https://mlsanity.com/paper/2402.16445/) (2024)
- [TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Models](https://mlsanity.com/paper/2309.06719/) (2023)
- [Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives](https://mlsanity.com/paper/2407.14962/) (2024)
- [LLMs Are Prone to Fallacies in Causal Inference](https://mlsanity.com/paper/2406.12158/) (2024)
- [AbductionRules: Training Transformers to Explain Unexpected Inputs](https://mlsanity.com/paper/2203.12186/) (2022)
- [Can LLM-Augmented autonomous agents cooperate?, An evaluation of their cooperative capabilities through Melting Pot](https://mlsanity.com/paper/2403.11381/) (2024)
- [Information Re-Organization Improves Reasoning in Large Language Models](https://mlsanity.com/paper/2404.13985/) (2024)
- [Abductive Reasoning as Self-Supervision for Common Sense Question Answering](https://mlsanity.com/paper/1909.03099/) (2019)

## Built on by (most cited first, 9 of 9)

- [A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems](https://mlsanity.com/paper/2504.09037/) (2025)
- [CallNavi, A Challenge and Empirical Study on LLM Function Calling and Routing](https://mlsanity.com/paper/2501.05255/) (2025)
- [From Reasoning to Learning: A Survey on Hypothesis Discovery and Rule Learning with Large Language Models](https://mlsanity.com/paper/2505.21935/) (2025)
- [Wiring the 'Why': A Unified Taxonomy and Survey of Abductive Reasoning in LLMs](https://mlsanity.com/paper/2604.08016/) (2026)
- [AILS-NTUA at SemEval-2026 Task 12: Graph-Based Retrieval and Reflective Prompting for Abductive Event Reasoning](https://mlsanity.com/paper/2603.04319/) (2026)
- [Investigating Advanced Reasoning of Large Language Models via Black-Box Environment Interaction](https://mlsanity.com/paper/2508.19035/) (2025)
- [IFDNS: An Iterative Feedback-Driven Neuro-Symbolic Method for Faithful Logical Reasoning](https://mlsanity.com/paper/2601.07464/) (2026)
- [GR-Ben: A General Reasoning Benchmark for Evaluating Process Reward Models](https://mlsanity.com/paper/2605.01203/) (2026)
- [CEDAR-GRPO: Process-Aware Reinforcement Learning for General Abductive Reasoning in LLMs](https://mlsanity.com/paper/2608.14791/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have developed rapidly, and their reasoning capabilities have become a hot research topic. However, there is still limited exploration of abductive reasoning. The multi-perspective and multi-level of causes is one of the core challenges of abductive reasoning, which cannot be solved well by existing methods. We construct a specialized dataset named DeepAbduction, which is designed for tracing the causes of pollution and disease, addressing the lack of datasets in this field. We propose Inverse-Forward Abductive Reasoning (IFAR) framework for LLMs multi-perspective and multi-level abductive reasoning. IFAR is zero-shot and combines generalized backward reasoning with relation-by-relation forward verification. Experimental results show that IFAR achieves an improvement of approximately 40% in the F1 score compared to other methods under mainstream LLMs, while maintaining a balance between recall and precision. Furthermore, IFAR enhances the performance of non-reasoning LLMs to surpass LLMs which have been trained for reasoning, and remains effective when applied to the latter. Code will be released after the acceptance of our work.