1. A computer-implemented method for using machine-learning techniques to present a user interface with campaign spend allocations based on attribution of small signal stimulus in noisy response channels, the method comprising: generating, in a computer, from stimuli data associated with at least one channel, a plurality of time series stimuli data vectors from event notifications for an event propagated through a channel over a period of time to a plurality of users, the event notifications being associated with a plurality of small signal attributes specified at a sub-channel level of the channel; generating, from aggregated response data associated with the event and including a plurality of user responses to the event, a plurality of time series response data vectors from responses elicited over time; generating, using machine-learning techniques in a computer, a small signal correlation engine, to correlate at least one of the time series stimuli data vectors to one of the time series response data vectors, and generate a plurality of correlation coefficients that correlate the event notifications to the aggregated response data; generating, using machine-learning techniques in a computer, a learning model to simulate variations of the stimuli data based on stimuli and aggregated response data to predict user responses using the correlation coefficients, including computing a contribution value of at least one of the plurality of small signal attributes of at least one of the event notifications; updating the learning model based on a comparison of the predicted user responses to actual user responses to the event; generating a media spend allocation for the channel and at least one of a sub-channel associated with the channel or another channel based on the contributions of the event notifications associated with the channel and the at least one of the sub-channel associated with the channel or the other channel and a budget received via a user interface; displaying the media spend allocation to a user via the user interface; simulating an effect of an adjustment to the media spend allocation, received via the user interface, based on the predicted user responses simulated by the learning model; and presenting, via the user interface, the adjusted media spend allocation and predicted user response activity based on the adjustment determined by the simulating.