Agri-Hub
In the aforementioned work we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators. 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 the causal graph of the farm system, we use the back-door criterion to identify the impact of recommendations on the yield and subsequently we estimate it using several methods on observational data (linear regression, matching, inverse propensity score weighting and meta-learners). Our results showed that a field sown according to our recommendations enjoyed a significant increase in yield (12% to 17%). This repository contains the code & data in order to reproduce the results of the paper either to use the code or the dataset for further experimentation.
Country Applicable
Global
Authors/Contributors
Ilias Tsoumas