Robust Evaluation of Customer Personalization Algorithms in Healthcare Using Causal Inference and Offline Policy Testing

Abstract

 Because clinical environments are safety-critical and heavily constrained by privacy, regulation, and limited experimentation capacity, many organizations evaluate personalization strategies using historical observational data rather than prospective randomized trials. This paper develops a technical framework for robust evaluation of customer personalization algorithms in healthcare using causal inference and offline policy testing. We formalize personalization as a policy that maps patient context to actions such as messaging intensity, appointment scheduling options, or care-management enrollment. We analyze identifiability under potential outcomes, clarify assumptions required for counterfactual generalization, and highlight how standard predictive validation can systematically misestimate patient benefit under selection and feedback. We then derive offline policy value estimators based on importance sampling, direct modeling, and doubly robust constructions, emphasizing variance control, positivity diagnostics, cross-fitting, and uncertainty quantification. Robustness is treated as a first-class objective through sensitivity analysis for unmeasured confounding, distribution shift between logging and deployment populations, and outcome missingness mechanisms common in longitudinal healthcare data. Finally, we translate these methods into an operational evaluation workflow that supports governance, fairness auditing, and safety constraints without relying on live experimentation. The resulting approach supports comparative testing of candidate personalization algorithms while explicitly characterizing the causal and statistical conditions under which offline estimates are reliable.