In an embodiment, a feature that is different between variations of web-based electronic content can be determined to be a true and significant cause of a difference of a measured metric when three conditions are met. The three conditions may be satisfied when a probability value for a false positive is less than a false positive threshold value, a statistical power is greater than a statistical power threshold value, and a difference between mean values determined for distributions is greater than a mean threshold value. In a further embodiment, and when there is no control group, a causal model can be generated for different interventions or values related to a desired outcome. The causal model can be executed to determine which intervention is most impactful on the desired outcome and/or to determine an overall impact of a categorical variable on the desired outcome.

A method, non-transitory computer readable medium, device and system that maintains aggregate visit data comprising group visit data for a plurality of groups and a plurality of affiliate websites based on processed request data for a plurality of requests. First request data corresponding to a first request from a client to a web server system is received. At least a first group from the plurality of groups is determined based on the first request data. First group visit data for the first group is obtained from the aggregate visit data. Affinity data comprising affinity scores for the affiliate websites is generated based on the first group visit data and is used to determine affiliate content served in association with web content served by the web server system.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing application program interface calls.