  This paper provides a link between causal inference and machine learning
techniques - specifically, Classification and Regression Trees (CART) - in
observational studies where the receipt of the treatment is not randomized, but
the assignment to the treatment can be assumed to be randomized (irregular
assignment mechanism). The paper contributes to the growing applied machine
learning literature on causal inference, by proposing a modified version of the
Causal Tree (CT) algorithm to draw causal inference from an irregular
assignment mechanism. The proposed method is developed by merging the CT
approach with the instrumental variable framework to causal inference, hence
the name Causal Tree with Instrumental Variable (CT-IV). As compared to CT, the
main strength of CT-IV is that it can deal more efficiently with the
heterogeneity of causal effects, as demonstrated by a series of numerical
results obtained on synthetic data. Then, the proposed algorithm is used to
evaluate a public policy implemented by the Tuscan Regional Administration
(Italy), which aimed at easing the access to credit for small firms. In this
context, CT-IV breaks fresh ground for target-based policies, identifying
interesting heterogeneous causal effects.
