# The Impact of Missing Data on Causal Discovery: A Multicentric Clinical Study

arXiv: https://arxiv.org/abs/2305.10050 · HTML: https://mlsanity.com/paper/2305.10050/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 2 of 2)

- [Graphical Models for Processing Missing Data](https://mlsanity.com/paper/1801.03583/) (2018)
- [Greedy structure learning from data that contain systematic missing values](https://mlsanity.com/paper/2107.04184/) (2021)

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

- [Causal Discovery on Higher-Order Interactions](https://mlsanity.com/paper/2511.14206/) (2025)
- [Towards a Transportable Causal Network Model Based on Observational Healthcare Data](https://mlsanity.com/paper/2311.08427/) (2023)
- [Towards Privacy-Aware Bayesian Networks: A Credal Approach](https://mlsanity.com/paper/2509.18949/) (2025)

## Abstract (arXiv)

Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are not usually available. Furthermore, observational data may contain missing values, which impact the recovery of the causal graph by causal discovery algorithms: a crucial issue often ignored in clinical studies. In this work, we use data from a multi-centric study on endometrial cancer to analyze the impact of different missingness mechanisms on the recovered causal graph. This is achieved by extending state-of-the-art causal discovery algorithms to exploit expert knowledge without sacrificing theoretical soundness. We validate the recovered graph with expert physicians, showing that our approach finds clinically-relevant solutions. Finally, we discuss the goodness of fit of our graph and its consistency from a clinical decision-making perspective using graphical separation to validate causal pathways.