  This paper focuses on the expected difference in borrower's repayment when
there is a change in the lender's credit decisions. Classical estimators
overlook the confounding effects and hence the estimation error can be
magnificent. As such, we propose another approach to construct the estimators
such that the error can be greatly reduced. The proposed estimators are shown
to be unbiased, consistent, and robust through a combination of theoretical
analysis and numerical testing. Moreover, we compare the power of estimating
the causal quantities between the classical estimators and the proposed
estimators. The comparison is tested across a wide range of models, including
linear regression models, tree-based models, and neural network-based models,
under different simulated datasets that exhibit different levels of causality,
different degrees of nonlinearity, and different distributional properties.
Most importantly, we apply our approaches to a large observational dataset
provided by a global technology firm that operates in both the e-commerce and
the lending business. We find that the relative reduction of estimation error
is strikingly substantial if the causal effects are accounted for correctly.
