Practical deep-dives into causal inference, experiment design, and predictive modeling. From A/B testing methodology to LTV prediction and ad auction mechanics — the core topics every data analyst needs to master.
Learn how to apply Two-Way Fixed Effects (TWFE) models to estimate interaction effects between product color and search queries. We cover the bias pitfalls of TWFE under staggered adoption and practical strategies for robust causal estimation in e-commerce settings.
Running experiments without a fixed sample size inflates Type I error rates. This article explains how alpha spending functions keep false positive rates under control, using a practical two-look scenario with interim analyses spaced one week apart.
When you test many metrics simultaneously, your Family-Wise Error Rate explodes. This article compares Bonferroni correction and Benjamini-Hochberg FDR control, with concrete guidance on which to apply in recommendation and advertising experiments.
Experiment duration affects result validity more than most practitioners realize. We explore novelty effects, seasonality, and user learning curves, then lay out statistical and business criteria for choosing the right experiment window.
With 1,000 users and 4 binary features, you can construct up to 16 strata — but should you? This article walks through the variance reduction benefits of stratified sampling and the sparsity trade-offs that emerge when strata become too granular.
CUPED (Controlled-experiment Using Pre-Experiment Data) is widely used to reduce variance in A/B tests, but its theoretical foundation is rarely discussed. This article formally shows its equivalence to the Frisch-Waugh-Lovell theorem and explains why covariate adjustment preserves unbiasedness while shrinking variance.
Predicting long-term LTV from short-term behavioral signals is one of the hardest problems in growth analytics. We compare the causal surrogate index framework from Athey et al. (2025) with deep learning approaches from Google researchers, highlighting when each method is appropriate.
Predicting who will purchase is not the same as predicting who will purchase because of your campaign. This article explains the difference between response modeling and uplift modeling (CATE estimation), compares T-Learner, S-Learner, and X-Learner architectures, and shows how to maximize ROI on CRM interventions like retention coupons.
Ghost bidding simulates counterfactual ad exposures for losing bids, enabling causal measurement of ad effectiveness without the interference problems that plague standard A/B tests. We explain the mechanics of ghost bidding within auction systems and compare it to holdout-based experimental designs.