  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%).
