---
license: mit
language:
- en
tags:
- knowledge-graph
- causal-inference
- rag
- zero-hallucination
- triplets
pretty_name: dotcausal Dataset Loader
size_categories:
- n<1K
---

# dotcausal - HuggingFace Dataset Loader

Load `.causal` binary knowledge graph files as HuggingFace Datasets.

## What is .causal?

The `.causal` format is a binary knowledge graph with **embedded deterministic inference**. It solves the fundamental problem of AI-assisted discovery: **LLMs hallucinate, databases don't reason**.

| Technology | What it does | What's missing |
|------------|--------------|----------------|
| **SQLite** | Stores facts | No reasoning |
| **Vector RAG** | Finds similar text | No logic |
| **LLMs** | Reasons creatively | Hallucination risk |
| **.causal** | Stores + Reasons | **Zero hallucination** |

### Key Features

- **30-40x faster queries** than SQLite
- **50-200% fact amplification** through transitive chains
- **Zero hallucination** - pure deterministic logic
- **Full provenance** - trace every inference

## Installation

```bash
pip install datasets dotcausal
```

## Usage

### Load from local .causal file

```python
from datasets import load_dataset

# Load your .causal file
ds = load_dataset("chkmie/dotcausal", data_files="knowledge.causal")

print(ds["train"][0])
# {'trigger': 'SARS-CoV-2', 'mechanism': 'damages', 'outcome': 'mitochondria',
#  'confidence': 0.9, 'is_inferred': False, 'source': 'paper_A.pdf', 'provenance': []}
```

### With configuration

```python
# Only explicit triplets (no inferred)
ds = load_dataset(
    "chkmie/dotcausal",
    "explicit_only",
    data_files="knowledge.causal",
)

# High confidence only (>= 0.8)
ds = load_dataset(
    "chkmie/dotcausal",
    "high_confidence",
    data_files="knowledge.causal",
)
```

### Multiple files / splits

```python
ds = load_dataset(
    "chkmie/dotcausal",
    data_files={
        "train": "train_knowledge.causal",
        "test": "test_knowledge.causal",
    },
)
```

## Dataset Schema

| Field | Type | Description |
|-------|------|-------------|
| `trigger` | string | The cause/trigger entity |
| `mechanism` | string | The relationship type |
| `outcome` | string | The effect/outcome entity |
| `confidence` | float32 | Confidence score (0-1) |
| `is_inferred` | bool | Whether derived or explicit |
| `source` | string | Original source (e.g., paper) |
| `provenance` | list[string] | Source triplets for inferred facts |

## Creating .causal Files

```python
from dotcausal import CausalWriter

writer = CausalWriter()
writer.add_triplet(
    trigger="SARS-CoV-2",
    mechanism="damages",
    outcome="mitochondria",
    confidence=0.9,
    source="paper_A.pdf",
)
writer.save("knowledge.causal")
```

## References

- **PyPI**: https://pypi.org/project/dotcausal/
- **GitHub**: https://github.com/DT-Foss/dotcausal
- **Whitepaper**: https://doi.org/10.5281/zenodo.18326222

## Citation

```bibtex
@article{foss2026causal,
  author = {Foss, David Tom},
  title = {The .causal Format: Deterministic Inference for AI-Assisted Hypothesis Amplification},
  journal = {Zenodo},
  year = {2026},
  doi = {10.5281/zenodo.18326222}
}
```

## License

MIT