# dcFCI: Robust Causal Discovery Under Latent Confounding, Unfaithfulness, and Mixed Data

arXiv: https://arxiv.org/abs/2505.06542 · HTML: https://mlsanity.com/paper/2505.06542/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 49 references that are in the map.
- First posted on arXiv 10 May 2025.

## Builds on (most cited first, 3 of 3)

- [Differentiable Causal Discovery Under Unmeasured Confounding](https://mlsanity.com/paper/2010.06978/) (2020)
- [Ancestral Causal Inference](https://mlsanity.com/paper/1606.07035/) (2016)
- [The Structurally Complex with Additive Parent Causality (SCARY) Dataset](https://mlsanity.com/paper/2304.14109/) (2023)

## Built on by (most cited first, 1 of 1)

- [Smoothing the Landscape: Causal Structure Learning via Diffusion Denoising Objectives](https://mlsanity.com/paper/2604.02250/) (2026)

## Abstract (arXiv)

Causal discovery is central to inferring causal relationships from observational data. In the presence of latent confounding, algorithms such as Fast Causal Inference (FCI) learn a Partial Ancestral Graph (PAG) representing the true model's Markov Equivalence Class. However, their correctness critically depends on empirical faithfulness, the assumption that observed (in)dependencies perfectly reflect those of the underlying causal model, which often fails in practice due to limited sample sizes. To address this, we introduce the first nonparametric score to assess a PAG's compatibility with observed data, even with mixed variable types. This score is both necessary and sufficient to characterize structural uncertainty and distinguish between distinct PAGs. We then propose data-compatible FCI (dcFCI), the first hybrid causal discovery algorithm to jointly address latent confounding, empirical unfaithfulness, and mixed data types. dcFCI integrates our score into an (Anytime)FCI-guided search that systematically explores, ranks, and validates candidate PAGs. Experiments on synthetic and real-world scenarios demonstrate that dcFCI significantly outperforms state-of-the-art methods, often recovering the true PAG even in small and heterogeneous datasets. Examining top-ranked PAGs further provides valuable insights into structural uncertainty, supporting more robust and informed causal reasoning and decision-making.