Estimating causal effects of credit decisions

In principle, making credit decisions under uncertainty can be approached by estimating the potential future outcomes that will result from the various decision alternatives. In practice, estimation difficulties may arise as a result of selection bias and limited historic testing. We review some theoretical results and practical estimation tools from observation study design and causal modeling, and evaluate their relevance to credit decision problems. Building on these results and tools, we propose a novel approach for estimating potential outcomes for credit decisions with multiple alternatives based on matching on multiple propensity scores. We demonstrate the approach and discuss results for risk-based pricing and credit line increase problems. Among the strengths of our approach are its transparency about data support for the estimates and its ability to incorporate prior knowledge in the extrapolative inference of treatment-response curves.