Why Prevention Data Is Rejected by Payers
Imagine an enterprise runs a cardiovascular health program for 1,000 employees. After one year, participants' average cardiovascular risk score dropped 12%, versus 3% for non-participants. The difference looks significant.
But when the enterprise takes this data to negotiate insurance discounts, the actuary asks a devastating question: How do you prove this 12% improvement was caused by your intervention, rather than by the fact that health-conscious employees self-selected into the program?
This is selection bias.
Selection Bias: The Fatal Flaw of Correlation Evidence
People who actively join health management programs tend to already be healthier before joining — stronger health awareness, more regular habits. In other words, the two groups were not comparable before the intervention even started.
Core Issue:Correlation can tell you "participants are healthier than non-participants," but cannot tell you "they are healthier because they participated." Payers need the latter — causal evidence.
PSM: The Tool to Bridge the Methodological Gap
PSM's core idea: use statistical methods to find a control group member with highly similar baseline characteristics for each treatment group member. After matching, the two groups become comparable on observable characteristics, eliminating observable selection bias. The effect difference calculated on matched pairs then has causal interpretability.
The difference between correlation and causation is not a statistical technicality — it is the fundamental prerequisite for whether payers will accept the evidence. Without causal evidence, prevention's commercial value cannot be formally recognized.