A Human-Augmenting Agentic Workflow for Causal Inference

By Winston Chou, Adrien Alexandre, Lars Olds, Yi Zhang, Garrett Hagemann, and Nathan Kallus Introduction Imagine asking a data agent to analyze the causal relationship between two variables, such as the effect of watching a popular Netflix show on long-term member retention. It queries your data, runs a regression, and confidently returns an answer. How much should you trust it? Can you be confident that the agent accounted for subtle biases — or does it treat passionate fans as if they were the average vie