1. A method comprising: conducting a campaign experiment that reflects implementation of a campaign change in an experiment environment to generate experimental results that reflect a causal measurement of value of a campaign, wherein the campaign change includes a current change to one or more campaign parameters for providing content items over a computer network for presentation by user computing devices; determining, by one or more computing servers, a measure of effectiveness of the campaign change to within a predetermined confidence level, based at least in part on analyzing the experimental results; determining, by the one or more computing servers, an estimate of effectiveness of the campaign change, wherein determining the estimate of effectiveness comprises referencing campaign data related to the campaign change, referencing interaction data that indicates user interactions with one or more of the content items, correlating the campaign data with the interaction data over a predetermined time period, and applying rules of an identified attribution model that assigns credit to user interactions with one or more of the content items that lead to user conversions; comparing, by the one or more computing servers, the determined estimate of effectiveness of the campaign change to the determined measure of effectiveness of the campaign change; determining, based on the comparison, that the identified attribution model provides an estimate of the measure of effectiveness that is within a predetermined range of the measure of effectiveness of the campaign change; for one or more subsequent campaign changes that include a change to the one or more campaign parameters that matches the current change to the one or more campaign parameters, determining a subsequent estimate of effectiveness of the subsequent campaign change, based at least in part on applying the identified attribution model in lieu of conducting another campaign experiment to determine the subsequent estimate of effectiveness of the subsequent campaign change; and using subsequent determined estimates of effectiveness of the subsequent campaign changes obtained from application of the identified attribution model as a proxy for measures of effectiveness in lieu of conducting another campaign experiment to determine the measure of effectiveness.
1. A method comprising: conducting a campaign experiment that reflects implementation of a campaign change in an experiment environment to generate experimental results that reflect a causal measurement of value of a campaign, wherein the campaign change includes a current change to one or more campaign parameters for providing content items over a computer network for presentation by user computing devices; determining, by one or more computing servers, a measure of effectiveness of the campaign change to within a predetermined confidence level, based at least in part on analyzing the experimental results; determining, by the one or more computing servers, an estimate of effectiveness of the campaign change, wherein determining the estimate of effectiveness comprises referencing campaign data related to the campaign change, referencing interaction data that indicates user interactions with one or more of the content items, correlating the campaign data with the interaction data over a predetermined time period, and applying rules of an identified attribution model that assigns credit to user interactions with one or more of the content items that lead to user conversions; comparing, by the one or more computing servers, the determined estimate of effectiveness of the campaign change to the determined measure of effectiveness of the campaign change; determining, based on the comparison, that the identified attribution model provides an estimate of the measure of effectiveness that is within a predetermined range of the measure of effectiveness of the campaign change; for one or more subsequent campaign changes that include a change to the one or more campaign parameters that matches the current change to the one or more campaign parameters, determining a subsequent estimate of effectiveness of the subsequent campaign change, based at least in part on applying the identified attribution model in lieu of conducting another campaign experiment to determine the subsequent estimate of effectiveness of the subsequent campaign change; and using subsequent determined estimates of effectiveness of the subsequent campaign changes obtained from application of the identified attribution model as a proxy for measures of effectiveness in lieu of conducting another campaign experiment to determine the measure of effectiveness. 7. The method of claim 1 , further comprising identifying a plurality of attribution models, wherein comparing the determined estimate of effectiveness of the campaign change to the determined measure of effectiveness of the campaign change includes comparing a determined estimate of effectiveness of the campaign change for each of the identified plurality of attribution models to the determined measure of effectiveness of the campaign change, and wherein determining that the identified attribution model provides an estimate of the measure of effectiveness includes determining the identified attribution model from among the plurality of attribution models as an attribution model that indicates an estimate of the measure of effectiveness that is closest to the determined measure of effectiveness of the campaign change.