Abstract: Embodiments are described herein for a compute resource-efficient technique for estimating a measurable effect attributable to participating in an activity. The foregoing is achieved by analyzing previously-collected features comprising different items of information associated with millions of users. The users comprise a first group that have participated in the activity and a second group that have not participated in the activity. Machine learning-based techniques are utilized to match users from each of the groups, with the goal being to match users having the same distribution of features. In particular, machine learning-based techniques are utilized to determine a propensity score for each user based on the features. Users from the first group are matched to users from the second group having a propensity score falling within a particular range. The measurable effect is determined based on an analysis of an average participation level of users matched in each range.