Meta is considering a new advertiser-facing ad management feature. When the system detects that an advertiser's Ads Pixel may be misconfigured or sending poor-quality signals, the advertiser receives an in-product notification explaining the issue and suggesting a fix. The Pixel is used for conversion tracking and optimization, so better Pixel health could improve both measurement quality and ad delivery. However, the feature could also have downsides: false alarms, alert fatigue, unnecessary support tickets, or advertisers becoming nervous and reducing spend. How would you evaluate whether this feature is good or bad? In your answer, discuss: - the product hypothesis and causal chain - the right experiment or quasi-experiment design - the correct unit of randomization and eligible population - primary metrics, secondary metrics, and guardrail metrics - how to define "Pixel signal quality" and "ads performance" - how to handle selection bias, measurement artifacts, and cases where measured conversions improve only because tracking improved - how you would interpret conflicting results, such as better Pixel health but worse short-term spend