# Online Causal Structure Learning in the Presence of Latent Variables

## TL;DR

This paper introduces two online algorithms for causal structure learning that adapt to changing causal relationships in real-time, effectively handling latent variables and outperforming traditional methods.

## Contribution

The paper presents novel online algorithms capable of tracking dynamic causal structures with latent variables, improving accuracy and efficiency over existing batch methods.

## Key findings

- Outperformed standard FCI in accuracy on synthetic data.
- Effectively handled latent variables in real-world datasets.
- Revised correlation values without reprocessing entire datasets.

## Abstract

We present two online causal structure learning algorithms which can track changes in a causal structure and process data in a dynamic real-time manner. Standard causal structure learning algorithms assume that causal structure does not change during the data collection process, but in real-world scenarios, it does often change. Therefore, it is inappropriate to handle such changes with existing batch-learning approaches, and instead, a structure should be learned in an online manner. The online causal structure learning algorithms we present here can revise correlation values without reprocessing the entire dataset and use an existing model to avoid relearning the causal links in the prior model, which still fit data. Proposed algorithms are tested on synthetic and real-world datasets, the latter being a seasonally adjusted commodity price index dataset for the U.S. The online causal structure learning algorithms outperformed standard FCI by a large margin in learning the changed causal structure correctly and efficiently when latent variables were present.