Heterogeneous Treatment Effects on Adventures in Why

Interpretable and Validatable Uplift Modeling

bob@ConvexAnalytics.com (Bob Wilson) — Tue, 27 Oct 2026 00:00:00 +0000

Interpretable and Validatable Uplift Modeling

bob@ConvexAnalytics.com (Bob Wilson) — Sat, 15 Jun 2024 00:00:00 +0000

In this note, we introduce a method for interpreting and validating the results of uplift modeling. We propose two novel strategies for controlling the Familywise Error Rate in this setting.

User Segmentation from Heterogeneous Treatment Effects

bob@ConvexAnalytics.com (Bob Wilson) — Mon, 21 Feb 2022 00:00:00 +0000

Imagine we are attempting to identify segments within an audience, perhaps so we can market to them more effectively through personalization. A common approach to doing so is to apply some kind of clustering algorithm (such as K-means) based on various user covariates. I have never been especially happy with this approach: the resulting clusters seem pretty arbitrary.

Heterogeneous Treatment Effect Estimation: Function Approximation

bob@ConvexAnalytics.com (Bob Wilson) — Mon, 10 Jan 2022 00:00:00 +0000

A simple approach to heterogeneous treatment effect estimation relies on a difference in approximations to the outcome function among the two treatment groups. In this post, I derive the conditions under which this approach works.