  Post-harvest diseases of apple are one of the major issues in the economical
sector of apple production, causing severe economical losses to producers.
Thus, we developed DSSApple, a picture-based decision support system able to
help users in the diagnosis of apple diseases. Specifically, this paper
addresses the problem of sequentially optimizing for the best diagnosis,
leveraging past interactions with the system and their contextual information
(i.e. the evidence provided by the users). The problem of learning an online
model while optimizing for its outcome is commonly addressed in the literature
through a stochastic active learning paradigm - i.e. Contextual Multi-Armed
Bandit (CMAB). This methodology interactively updates the decision model
considering the success of each past interaction with respect to the context
provided in each round. However, this information is very often partial and
inadequate to handle such complex decision making problems. On the other hand,
human decisions implicitly include unobserved factors (referred in the
literature as unobserved confounders) that significantly contribute to the
human's final decision. In this paper, we take advantage of the information
embedded in the observed human decisions to marginalize confounding factors and
improve the capability of the CMAB model to identify the correct diagnosis.
Specifically, we propose a Counterfactual Contextual Multi-Armed Bandit, a
model based on the causal concept of counterfactual. The proposed model is
validated with offline experiments based on data collected through a large user
study on the application. The results prove that our model is able to
outperform both traditional CMAB algorithms and observed user decisions, in
real-world tasks of predicting the correct apple disease.
