Causal Inference and Experiment Design in Kuaishou Live Streaming: Methods and Case Studies
This article explains how Kuaishou applies causal inference frameworks, such as Rubin's potential outcomes and Pearl's causal graphs, together with machine‑learning techniques like double‑machine learning, causal forests, and meta‑learners to evaluate product features, recommendation strategies, and user behavior under complex network effects in live streaming.