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.

[1]  Andrew Gelman,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models , 2006 .

[2]  D. Rubin.  Matched Sampling for Causal Effects , 2006 .

[3]  P. Holland.  Statistics and Causal Inference , 1985 .

[4]  Michael Lechner.  A Note on the Common Support Problem in Applied Evaluation Studies , 2000 .

[5]  Frank E. Harrell,et al.  Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2001 .

[6]  M. Lechner.  Identification and Estimation of Causal Effects of Multiple Treatments Under the Conditional Independence Assumption , 1999, SSRN Electronic Journal.

[7]  Kneale T. Marshall,et al.  Decision making and forecasting , 1995 .

[8]  Donald Rubin,et al.  Estimating Causal Effects from Large Data Sets Using Propensity Scores , 1997, Annals of Internal Medicine.

[9]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[10]  Donald B. Rubin,et al.  Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology , 2006 .

[11]  G. Imbens.  The Role of the Propensity Score in Estimating Dose-Response Functions , 1999 .

[12]  Richard K. Crump,et al.  Moving the Goalposts: Addressing Limited Overlap in Estimation of Average Treatment Effects by Changing the Estimand , 2006, SSRN Electronic Journal.

[13]  Larry E. Rosenberger,et al.  The Deciding Factor , 2009 .

[14]  L. Thomas.  Consumer credit models , 2009 .