Machine learning based causal inference is a powerful tool for the advertising industry. By leveraging the power of machine learning, companies can gain a deeper understanding of the causal relationships between their advertising efforts and consumer behavior, leading to more effective and efficient advertising campaigns.
This paper is intended for any audience looking to learn more about how machine learning can be used to measure the effects of interventions, understand the heterogeneous impact of interventions, and design targeted treatment assignment policies.
