Uplift Modeling: Quantifying Heterogeneous Treatment Effects at Kuaishou

This article introduces Kuaishou's exploration of uplift modeling for estimating heterogeneous treatment effects, discusses practical challenges such as continuous treatment variables and statistical inference for nonlinear models, presents a dual‑neural‑network solution with evaluation metrics, and showcases applications in fan growth and push notifications.

Dual Neural NetworkKuaishoucontinuous treatment

0 likes · 14 min read

Uplift Modeling: Quantifying Heterogeneous Treatment Effects at Kuaishou

DataFunTalk

May 13, 2021 · Artificial Intelligence

Continuous Causal Forest: Extending Uplift Modeling to Multivariate and Continuous Treatments

This article introduces the Continuous Causal Forest, a novel uplift modeling approach that expands binary treatment effect estimation to handle multivariate and continuous treatment variables, demonstrates its construction, evaluates its performance on ride‑hailing pricing strategies, and discusses its advantages, limitations, and future research directions.

Pricing strategyUplift Modelingcausal forest

0 likes · 9 min read

Continuous Causal Forest: Extending Uplift Modeling to Multivariate and Continuous Treatments

Didi Tech

May 11, 2021 · Artificial Intelligence

Continuous Causal Forest: Extending Uplift Modeling to Multi‑dimensional Continuous Treatments in Ride‑hailing Pricing

By extending binary causal forests with a Continuous Average Partial Effect statistic, the Continuous Causal Forest enables uplift modeling for multi‑dimensional continuous treatments such as ride‑hailing pricing, delivering superior Qini scores and over 15% ROI improvement while simplifying implementation and reducing deployment costs.

Pricing strategyRandom Forestcontinuous treatment

0 likes · 10 min read

Continuous Causal Forest: Extending Uplift Modeling to Multi‑dimensional Continuous Treatments in Ride‑hailing Pricing