# Causal machine learning methods for understanding land use and land cover change

## TL;DR

This paper explores how causal machine learning can help understand complex land use changes and improve policy design.

## Contribution

The paper introduces causal machine learning methods to the land use and land cover change research community.

## Key findings

- Causal ML methods can improve understanding of socio-ecological land use dynamics.
- Combining ML with domain knowledge is essential for effective policy design.
- Workshops identified promising ML approaches for LULCC analysis.

## Abstract

Understanding the roles of different drivers in land use and land cover change (LULCC) is a critical research challenge. However, as LULCC is the result of complex, socio-ecological processes and is highly context dependent, achieving such understanding is difficult. This is particularly true for causal modelling approaches that are critical for effective policy formulation. Causal machine learning (ML) methods could help address this challenge, but are as yet poorly understood or applied by the LULCC community.

To provide an accessible introduction to the state of the art for causal ML methods, their limitations, and their potential applications understanding LULCC.

We conducted two workshops where we identified the most promising ML methods for increasing understanding of LULCC dynamics.

We provide a brief overview of the challenges to causal modelling of LULCC, including a simple example, and the most relevant causal ML approaches for addressing these challenges, as well as their limitations.

Causal ML methods hold considerable promise for improving causal modelling of LULCC. However, the complexity of LULCC dynamics mean that such methods must be combined with domain understanding and qualitative insights for effective policy design.