  Model interpretability has become an important problem in machine learning
(ML) due to the increased effect that algorithmic decisions have on humans.
Counterfactual explanations can help users understand not only why ML models
make certain decisions, but also how these decisions can be changed. We frame
the problem of finding counterfactual explanations as a gradient-based
optimization task and extend previous work that could only be applied to
differentiable models. In order to accommodate non-differentiable models such
as tree ensembles, we use probabilistic model approximations in the
optimization framework. We introduce an approximation technique that is
effective for finding counterfactual explanations for predictions of the
original model and show that our counterfactual examples are significantly
closer to the original instances than those produced by other methods
specifically designed for tree ensembles.
