# Learning Causal Graphs in Manufacturing Domains using Structural Equation Models

arXiv: https://arxiv.org/abs/2210.14573 · HTML: https://mlsanity.com/paper/2210.14573/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://mlsanity.com/paper/1803.01422/) (2018)
- [Causal Discovery with Continuous Additive Noise Models](https://mlsanity.com/paper/1309.6779/) (2013)
- [CAM: Causal additive models, high-dimensional order search and penalized regression](https://mlsanity.com/paper/1310.1533/) (2013)
- [On the Relation between Prediction and Imputation Accuracy under Missing Covariates](https://mlsanity.com/paper/2112.05248/) (2021)
- [Causal Discovery for Manufacturing Domains](https://mlsanity.com/paper/1605.04056/) (2016)
- [Estimating Gaussian Copulas with Missing Data](https://mlsanity.com/paper/2201.05565/) (2022)

## Built on by (most cited first, 2 of 2)

- [Interactive and Intelligent Root Cause Analysis in Manufacturing with Causal Bayesian Networks and Knowledge Graphs](https://mlsanity.com/paper/2402.00043/) (2024)
- [Boosting Causal Additive Models](https://mlsanity.com/paper/2401.06523/) (2024)

## Abstract (arXiv)

Many production processes are characterized by numerous and complex cause-and-effect relationships. Since they are only partially known they pose a challenge to effective process control. In this work we present how Structural Equation Models can be used for deriving cause-and-effect relationships from the combination of prior knowledge and process data in the manufacturing domain. Compared to existing applications, we do not assume linear relationships leading to more informative results.