Abstract: Techniques for generating unbiased estimates of causal effects in online experiments are provided. In one technique, campaign data is received that includes a first set of targeting criteria and a second set of targeting criteria. An online experiment is established that comprises a content delivery campaign that is associated with a treatment group and a control group. Afterward, a content request is received and a first entity that initiated the content request is identified. In response to determining that the first entity is targeted by the campaign based on the first set of targeting criteria, the first entity is randomly assigned to the control group. In responses to second content request, a second entity is identified. In response to determining that the second entity is targeted by the campaign based on the first set of targeting criteria, the second entity is randomly assigned to the treatment group.

Abstract: Techniques for generating a multidimensional forecast are provided. In one technique, multiple segments are generated, each comprising a different set of attribute values. For each segment, a set of prior content requests for the segment is determined based on historical data, a forecasted number of content requests is determined based on the set of prior content requests, and the forecasted number of content requests is stored in association with a set of attribute values corresponding to the segment. A request is received to forecast performance of a content delivery campaign based on a particular set of attribute values. In response to receiving the request, multiple segments that share the particular set of attribute values are identified. The forecasted number of content requests associated with each segment of the multiple segments are aggregated to generate aggregated performance data. A portion of the aggregated performance data is caused to be displayed.