StatsTest Blog
Experimental design, data analysis, and statistical tooling for modern teams. No hype, just the math.
Causal Inference for Tech: When You Can't Run an Experiment
A practical guide to causal inference methods for product and data analysts when A/B tests aren't possible. Covers PSM, IV, RDD, DiD, and more.
Confounding: The One Thing That Breaks Every Observational Study
What confounding is, why it invalidates naive causal claims, and how to identify and handle confounders in product analytics and observational studies.
DAGs for Analysts: Drawing Your Assumptions Before You Analyze
How directed acyclic graphs help analysts identify confounders, avoid collider bias, and choose the right variables to control for in causal analysis.
Double/Debiased Machine Learning: Causal Effects with Flexible Models
How double/debiased machine learning combines ML flexibility with valid causal inference. Learn cross-fitting, Neyman orthogonality, and practical DML workflows.
Instrumental Variables: Finding Natural Experiments in Product Data
How instrumental variables let you estimate causal effects when unmeasured confounding makes direct comparison impossible. Practical IV examples for tech.
Mediation Analysis: Does Feature X Work Through Mechanism Y?
How mediation analysis identifies the causal mechanisms behind product effects. Learn when and how to decompose total effects into direct and indirect paths.
Propensity Score Matching: Balancing Groups Without Randomization
Learn how propensity score matching creates balanced comparison groups from observational data when randomized experiments aren't possible.
Regression Discontinuity: When Thresholds Create Experiments
How regression discontinuity designs exploit score cutoffs to estimate causal effects. A practical guide for product analysts with real-world examples.
