# Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients

arXiv: https://arxiv.org/abs/2303.06423 · HTML: https://mlsanity.com/paper/2303.06423/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 53 references that are in the map.
- First posted on arXiv 11 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [Causal Discovery with Continuous Additive Noise Models](https://mlsanity.com/paper/1309.6779/) (2013)
- [Detecting causal associations in large nonlinear time series datasets](https://mlsanity.com/paper/1702.07007/) (2017)
- [Learning Bayesian Networks: The Combination of Knowledge and Statistical Data](https://mlsanity.com/paper/1302.6815/) (2013)
- [A causal inference framework for cancer cluster investigations using publicly available data](https://mlsanity.com/paper/1811.05997/) (2018)

## Built on by (most cited first, 3 of 3)

- [Instrumental Variable Learning for Chest X-ray Classification](https://mlsanity.com/paper/2305.12070/) (2023)
- [Do Not Marginalize Mechanisms, Rather Consolidate!](https://mlsanity.com/paper/2310.08377/) (2023)
- [The causal relation between off-street parking and electric vehicle adoption in Scotland](https://mlsanity.com/paper/2604.09271/) (2026)

## Abstract (arXiv)

Discovering causal effects is at the core of scientific investigation but remains challenging when only observational data is available. In practice, causal networks are difficult to learn and interpret, and limited to relatively small datasets. We report a more reliable and scalable causal discovery method (iMIIC), based on a general mutual information supremum principle, which greatly improves the precision of inferred causal relations while distinguishing genuine causes from putative and latent causal effects. We showcase iMIIC on synthetic and real-life healthcare data from 396,179 breast cancer patients from the US Surveillance, Epidemiology, and End Results program. More than 90% of predicted causal effects appear correct, while the remaining unexpected direct and indirect causal effects can be interpreted in terms of diagnostic procedures, therapeutic timing, patient preference or socio-economic disparity. iMIIC's unique capabilities open up new avenues to discover reliable and interpretable causal networks across a range of research fields.