datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
CausalReasoningBenchmark
Automated Causal Reasoning Benchmark
Overview
The Automated Causal Reasoning Benchmark is a collection of real-world causal inference tasks drawn from 85 peer-reviewed research papers and three textbook-style collections (see CausalBenchmark.pdf). The benchmark contains 173 queries over 138 datasets. Each task is designed to evaluate both (i) identification, i.e., selecting an appropriate causal estimand and identification strategy given the study context, and (ii)… See the full description on the dataset page: https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark.Causal-Reasoning-Bench_CRBench
🦙 Causal Reasoning Benchmark (CRBench)
CRBench is a benchmark for evaluating process-level causal failures in
Chain-of-Thought (CoT) reasoning.
Rather than treating incorrect reasoning traces as homogeneous failures,
CRBench characterizes erroneous dependencies among intermediate reasoning
steps through a step-level causal-error taxonomy. It is designed to evaluate
whether reasoning methods can identify and correct structured causal failures
that arise during the reasoning… See the full description on the dataset page: https://huggingface.co/datasets/EdmondFU/Causal-Reasoning-Bench_CRBench.domain-agnostic-causal-reasoning-tuning
Domain-Agnostic Causal Reasoning Tuning Dataset
Training data for fine-tuning language models on multi-hop document reasoning. Each example is a graded reasoning trace produced by a frontier AI agent solving a procedurally generated challenge from the Botcoin proof-of-inference network.
The traces contain no real domain knowledge. Entities are fictional, numbers are random, and documents are generated deterministically from 128-bit seeds. The reasoning structure is what matters:… See the full description on the dataset page: https://huggingface.co/datasets/botcoinmoney/domain-agnostic-causal-reasoning-tuning.compositional_causal_reasoning
– 3k+ Hugging Face downloads –
https://jmaasch.github.io/ccr/
Causal reasoning and compositional reasoning are two core aspirations in AI. Measuring these behaviors requires principled
evaluation methods. Maasch et al. (2025) consider both behaviors simultaneously, under
the umbrella of compositional causal reasoning (CCR): the ability to infer how causal measures compose and, equivalently, how causal quantities propagate
through graphs. CCR.GB applies the… See the full description on the dataset page: https://huggingface.co/datasets/jmaasch/compositional_causal_reasoning.CausalReasoningBenchmark
Automated Causal Reasoning Benchmark
Anonymized release for double-blind review. Author, affiliation, and prior-whitepaper material have been removed. The data, solutions, and evaluation pipeline are otherwise identical to the version under review.
Overview
The Automated Causal Reasoning Benchmark is a collection of real-world causal inference tasks drawn from 85 peer-reviewed research papers and three textbook-style collections. The benchmark contains 173 queries over… See the full description on the dataset page: https://huggingface.co/datasets/anonsubmission16/CausalReasoningBenchmark.