Causal Inference Frameworks For Platform Integrity: Moving Beyond Correlation In Fraud And Abuse Detection

Abstract

Detecting fraudulent activity across large-scale digital platforms has long relied on correlation-driven classifiers built from labeled transaction records. While effective under stable conditions, such models degrade when policies shift, adversaries evolve, or label quality deteriorates through operational feedback cycles. This article contends that causal inference offers a more rigorous foundation for platform integrity work, allowing practitioners to evaluate the effects of interventions rather than merely surface statistical associations. Analytical methods covered span DAG specification, propensity score modeling, doubly robust procedures, and heterogeneous treatment effect estimation via causal forests, each situated within dispute resolution workflows and fraud governance practice. Publicly available evidence of cross-market performance variation anchors the empirical claims. Consumer fraud losses reported by the Federal Trade Commission exceeded ten billion US dollars in 2023; industry benchmarks place global e-commerce fraud losses at roughly 2.9 percent of revenue for that same year. A production readiness checklist closes the article, accompanied by discussion of unresolved problems in outcome labeling latency, instrument validity, and federated estimation across market boundaries.