# Evaluating Digital Agriculture Recommendations with Causal Inference

arXiv: https://arxiv.org/abs/2211.16938 · HTML: https://mlsanity.com/paper/2211.16938/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 78 references that are in the map.
- First posted on arXiv 30 Nov 2022.

## Builds on (most cited first, 7 of 7)

- [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)
- [Pest presence prediction using interpretable machine learning](https://mlsanity.com/paper/2205.07723/) (2022)
- [Personalizing Sustainable Agriculture with Causal Machine Learning](https://mlsanity.com/paper/2211.03179/) (2022)
- [Towards Space-to-Ground Data Availability for Agriculture Monitoring](https://mlsanity.com/paper/2205.07721/) (2022)

## Built on by (most cited first, 7 of 7)

- [Understanding the impacts of crop diversification in the context of climate change: a machine learning approach](https://mlsanity.com/paper/2307.08617/) (2023)
- [Assessing the Causal Impact of Humanitarian Aid on Food Security](https://mlsanity.com/paper/2310.11287/) (2023)
- [Causality and Explainability for Trustworthy Integrated Pest Management](https://mlsanity.com/paper/2312.04343/) (2023)
- [The Importance of Causality in Decision Making: A Perspective on Recommender Systems](https://mlsanity.com/paper/2410.01822/) (2024)
- [LLMs for Enhanced Agricultural Meteorological Recommendations](https://mlsanity.com/paper/2408.04640/) (2024)
- [Causal machine learning for sustainable agroecosystems](https://mlsanity.com/paper/2408.13155/) (2024)
- [Positive-Unlabeled Learning for Control Group Construction in Observational Causal Inference](https://mlsanity.com/paper/2507.14528/) (2025)

## Abstract (arXiv)

In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of smart farming tools. While AI-driven digital agriculture tools can offer high-performing predictive functionalities, they lack tangible quantitative evidence on their benefits to the farmers. Field experiments can derive such evidence, but are often costly, time consuming and hence limited in scope and scale of application. 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 (e.g., yield in this case). This way, we can increase farmers' trust via enhancing the transparency of the digital agriculture market and accelerate the adoption of technologies that aim to secure farmer income resilience and global agricultural sustainability. As a case study, we designed and implemented a recommendation system for the optimal sowing time of cotton based on numerical weather predictions, which was used by a farmers' cooperative during the growing season of 2021. We then leverage agricultural knowledge, collected yield data, and environmental information to develop a causal graph of the farm system. Using the back-door criterion, we identify the impact of sowing recommendations on the yield and subsequently estimate it using linear regression, matching, inverse propensity score weighting and meta-learners. The results reveal that a field sown according to our recommendations exhibited a statistically significant yield increase that ranged from 12% to 17%, depending on the method. The effect estimates were robust, as indicated by the agreement among the estimation methods and four successful refutation tests. We argue that this approach can be implemented for decision support systems of other fields, extending their evaluation beyond a performance assessment of internal functionalities.