  Online advertising has been the major monetization approach for Internet
companies. Advertisers invest budgets to bid for real-time impressions to gain
direct and indirect returns. Existing works have been concentrating on
optimizing direct returns brought by advertising traffic. However, indirect
returns induced by advertising traffic such as influencing the online organic
traffic and offline mouth-to-mouth marketing provide extra significant
motivation to advertisers. Modeling and quantization of causal effects between
the overall advertising return and budget enable the advertisers to spend their
money more judiciously. In this paper, we model the overall return as
individual advertising effect in causal inference with multiple treatments and
bound the expected estimation error with learnable factual loss and distance of
treatment-specific context distributions. Accordingly, a representation and
hypothesis network is used to minimize the loss bound. We apply the learned
causal effect in the online bidding engine of an industry-level sponsored
search system. Online experiments show that the causal inference based bidding
outperforms the existing online bidding algorithm.
