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

2026-07-13 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Social Sciences & Behavioral Studies · Depth: Intermediate, long

Summary

This article contrasts two distinct approaches to building predictive models: one based on latent constructs derived from indirect evidence, common in social science, and another utilizing observed behavioral signals from logged data, prevalent in industry. The author details their experience predicting "intention to engage" using Structural Equation Modeling (SEM) with latent variables like "privacy concern" and "perceived usefulness," which necessitate multiple survey items and a measurement model. In contrast, current work in ad measurement employs observed behavioral data, such as clicks or purchases, with models like XGBoost, where features are taken at face value. The piece highlights how modeling decisions, variable selection, and interpretation fundamentally invert between these two worlds, emphasizing that while behavioral data offers readily available variables, underlying validity questions persist and often manifest as production issues. Both approaches, however, demand upfront commitment to research questions and rigorous assumption checking.

Key takeaway

For Data Scientists or Machine Learning Engineers building predictive models, understand whether your target variable is a latent construct or a direct behavioral signal. If dealing with latent constructs, rigorously validate your measurement model to avoid biased coefficients. For behavioral data, explicitly document what each feature proxies; this helps diagnose model failures when proxies detach from true outcomes. Always commit to your question before seeing the answer and scrutinize implausible results as diagnostic signals.

Key insights

Modeling latent constructs requires building variables from indirect evidence, while behavioral data uses ready-made signals, fundamentally altering modeling approaches.

Principles

• Latent constructs require explicit measurement models.
• Behavioral features are proxies; their validity can decay.
• Model diagnostics depend on the model's purpose.

Method

Structural Equation Modeling (SEM) builds latent constructs from multiple indicators, while behavioral models like XGBoost use observed features directly, assuming recent behavior predicts future actions.

In practice

• Document what unobservable concept each behavioral feature proxies.
• Check historical seasonal divergence before trusting lift readouts.

Topics

• Latent Constructs
• Behavioral Data Modeling
• Structural Equation Modeling
• Predictive Analytics
• Measurement Validity
• Causal Inference

Best for: Data Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.