# Evaluating Digital Tools for Sustainable Agriculture using Causal Inference

arXiv: https://arxiv.org/abs/2211.03195 · HTML: https://mlsanity.com/paper/2211.03195/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 69 references that are in the map.
- First posted on arXiv 6 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [DoWhy: An End-to-End Library for Causal Inference](https://mlsanity.com/paper/2011.04216/) (2020)
- [CausalML: Python Package for Causal Machine Learning](https://mlsanity.com/paper/2002.11631/) (2020)
- [Towards assessing agricultural land suitability with causal machine learning](https://mlsanity.com/paper/2204.12956/) (2022)
- [Causal Inference in Geoscience and Remote Sensing from Observational Data](https://mlsanity.com/paper/2012.05150/) (2020)

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

- [Multi-Agent Geospatial Copilots for Remote Sensing Workflows](https://mlsanity.com/paper/2501.16254/) (2025)
- [DRCFS: Doubly Robust Causal Feature Selection](https://mlsanity.com/paper/2306.07024/) (2023)

## Abstract (arXiv)

In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of climate-smart farming tools. Even though AI-driven digital agriculture can offer high-performing predictive functionalities, it lacks tangible quantitative evidence on its benefits to the farmers. Field experiments can derive such evidence, but are often costly and time consuming. To this end, we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators. This way, we can increase farmers' trust by enhancing the transparency of the digital agriculture market, and in turn accelerate the adoption of technologies that aim to increase productivity and secure a sustainable and resilient agriculture against a changing climate. As a case study, we perform an empirical evaluation of a recommendation system for optimal cotton sowing, which was used by a farmers' cooperative during the growing season of 2021. We leverage agricultural knowledge to develop a causal graph of the farm system, we use the back-door criterion to identify the impact of recommendations on the yield and subsequently estimate it using several methods on observational data. The results show that a field sown according to our recommendations enjoyed a significant increase in yield (12% to 17%).