Building Models in Two Worlds: From Latent Constructs to Behavioral Signals

A data scientist compares modeling latent constructs (survey-based SEM in academic research) versus behavioral signals (ML-based propensity and lift models in industry ad measurement). The core insight is that the statistical machinery is nearly identical, but the context inverts almost every decision: where variables come from, what makes a variable valid, how features are selected, and what the model is expected to explain. Key practical lessons include treating nonsensical estimates as diagnostics rather than results, maintaining a proxy-to-construct mapping for behavioral features, understanding when multicollinearity matters (interpretation vs. prediction), and validating pre-period alignment against seasonal patterns before trusting lift estimates.

Source: https://towardsdatascience.com/building-models-in-two-worlds-from-latent-constructs-to-behavioral-signals. 8 Sync News only summarizes and links out; content copyright belongs to the authors and original sources.