Bayesian Causal Identification and Modeling of Advertising Conversion Paths

Within the internet advertising landscape, the intricate interplay between user exposure, clicks, and conversions often eludes precise measurement. Conventional analytical methods typically capture only superficial causal relationships, failing to accurately depict advertising effectiveness. This paper employs Bayesian causal modelling to construct a framework for identifying and predicting advertising conversions, probabilistically characterising influencing factors at each stage. By decomposing evaluations of diverse advertising pathways using predefined antecedents and consequents, the primary pathway is identified. Experimental results demonstrate that this method enables stable inference under incomplete information, provides more rational support for advertising optimisation, and offers a fresh perspective for exploring causal relationships in digital markets.