# Optimizing Data-driven Causal Discovery Using Knowledge-guided Search

arXiv: https://arxiv.org/abs/2304.05493 · HTML: https://mlsanity.com/paper/2304.05493/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 13 of 13)

- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://mlsanity.com/paper/1803.01422/) (2018)
- [DAG-GNN: DAG Structure Learning with Graph Neural Networks](https://mlsanity.com/paper/1904.10098/) (2019)
- [Gradient-Based Neural DAG Learning](https://mlsanity.com/paper/1906.02226/) (2019)
- [Causal Inference and Causal Explanation with Background Knowledge](https://mlsanity.com/paper/1302.4972/) (2013)
- [Causal Machine Learning for Healthcare and Precision Medicine](https://mlsanity.com/paper/2205.11402/) (2022)
- [gCastle: A Python Toolbox for Causal Discovery](https://mlsanity.com/paper/2111.15155/) (2021)
- [A Survey on Causal Discovery Methods for I.I.D. and Time Series Data](https://mlsanity.com/paper/2303.15027/) (2023)
- [Can Foundation Models Talk Causality?](https://mlsanity.com/paper/2206.10591/) (2022)
- [Evaluation Methods and Measures for Causal Learning Algorithms](https://mlsanity.com/paper/2202.02896/) (2022)
- [Selective Greedy Equivalence Search: Finding Optimal Bayesian Networks Using a Polynomial Number of Score Evaluations](https://mlsanity.com/paper/1506.02113/) (2015)
- [Evaluation of Induced Expert Knowledge in Causal Structure Learning by NOTEARS](https://mlsanity.com/paper/2301.01817/) (2023)
- [Structural Causal Model with Expert Augmented Knowledge to Estimate the Effect of Oxygen Therapy on Mortality in the ICU](https://mlsanity.com/paper/2010.14774/) (2020)
- [Domain Knowledge in A*-Based Causal Discovery](https://mlsanity.com/paper/2208.08247/) (2022)

## Built on by (most cited first, 5 of 5)

- [Cluster-Dags as Powerful Background Knowledge For Causal Discovery](https://mlsanity.com/paper/2512.10032/) (2025)
- [Less Greedy Equivalence Search](https://mlsanity.com/paper/2506.22331/) (2025)
- [Improving constraint-based discovery with robust propagation and reliable LLM priors](https://mlsanity.com/paper/2509.23570/) (2025)
- [PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning](https://mlsanity.com/paper/2605.07267/) (2026)
- [ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysis](https://mlsanity.com/paper/2605.27022/) (2026)

## Abstract (arXiv)

Learning causal relationships solely from observational data often fails to reveal the underlying causal mechanisms due to the vast search space of possible causal graphs, which can grow exponentially, especially for greedy algorithms using score-based approaches. Leveraging prior causal information, such as the presence or absence of causal edges, can help restrict and guide the score-based discovery process, leading to a more accurate search. In the healthcare domain, prior knowledge is abundant from sources like medical journals, electronic health records (EHRs), and clinical intervention outcomes. This study introduces a knowledge-guided causal structure search (KGS) approach that utilizes observational data and structural priors (such as causal edges) as constraints to learn the causal graph. KGS leverages prior edge information between variables, including the presence of a directed edge, the absence of an edge, and the presence of an undirected edge. We extensively evaluate KGS in multiple settings using synthetic and benchmark real-world datasets, as well as in a real-life healthcare application related to oxygen therapy treatment. To obtain causal priors, we use GPT-4 to retrieve relevant literature information. Our results show that structural priors of any type and amount enhance the search process, improving performance and optimizing causal discovery. This guided strategy ensures that the discovered edges align with established causal knowledge, enhancing the trustworthiness of findings while expediting the search process. It also enables a more focused exploration of causal mechanisms, potentially leading to more effective and personalized healthcare solutions.