As the online advertising industry has evolved into an age of diverse ad formats and delivery channels, users are exposed to complex ad treatments involving various ad characteristics. The diversity and generality of ad treatments call for accurate and causal measurement of ad effectiveness. In this talk, I will present a new causal inference framework for assessing the impact of general advertising treatments. It enables analysis on multi-dimensional ad treatments, where each ad treatment could be discrete or continuous. Our approach is unbiased and computationally efficient by employing a tree structure that specifies the relationship between user characteristics and the corresponding ad treatment. This framework is robust to model misspecification and highly flexible with minimal manual tuning. The efficacy of our approach is demonstrated in several advertising campaigns:

1. Investigate best frequency of ads in the campaign. We show that the ad frequency usually has a treatment effect cap and is often over-estimated by naive estimation. This investigation suggests adjusting the cap of the ad frequency to avoid wasting of ad inventory.
2. Investigate best ads size on different mobile devices. Our model suggests that the ad size 300*250 produces consistently high success rates across various mobile devices.