Integrating Causal Inference with Multi-Channel Attribution for Equitable Pharmaceutical Marketing Effectiveness Evaluation

Abstract

The pharmaceutical industry confronts significant challenges measuring marketing effectiveness across digital and traditional channels while meeting health equity mandates. Traditional attribution approaches inadequately address confounding variables in observational data, yielding suboptimal budget allocation and inequitable demographic reach. This research proposes an integrated framework combining causal inference with multi-channel attribution modeling for pharmaceutical marketing evaluation. The framework incorporates propensity score matching, Bayesian marketing mix modeling with adstock effects, and equity-aware stratification. Empirical validation demonstrates 23.4% higher predictive accuracy versus last-touch attribution while identifying effectiveness disparities across demographic subgroups. Findings provide actionable guidance for pharmaceutical marketers optimizing resource allocation while ensuring equitable medical information access across patient populations.