  Data Envelopment Analysis (DEA) allows us to capture the complex relationship
between multiple inputs and outputs in firms and organizations. Unfortunately,
managers may find it hard to understand a DEA model and this may lead to
mistrust in the analyses and to difficulties in deriving actionable information
from the model. In this paper, we propose to use the ideas of target setting in
DEA and of counterfactual analysis in Machine Learning to overcome these
problems. We define DEA counterfactuals or targets as alternative combinations
of inputs and outputs that are close to the original inputs and outputs of the
firm and lead to desired improvements in its performance. We formulate the
problem of finding counterfactuals as a bilevel optimization model. For a rich
class of cost functions, reflecting the effort an inefficient firm will need to
spend to change to its counterfactual, finding counterfactual explanations
boils down to solving Mixed Integer Convex Quadratic Problems with linear
constraints. We illustrate our approach using both a small numerical example
and a real-world dataset on banking branches.
