heterogeneous treatment effect

DataFunSummit

Jun 18, 2023 · Artificial Intelligence

Generalized Causal Forest: Construction and Application in Online Trading Markets

This article introduces the generalized causal forest, explains its non‑parametric nonlinear construction for estimating heterogeneous dose‑response functions, compares it with existing methods, and demonstrates its experimental results and deployment in an online ride‑hailing pricing system to balance supply and demand.

Generalized Causal ForestMachine Learningcausal inference

0 likes · 7 min read

Generalized Causal Forest: Construction and Application in Online Trading Markets

DataFunTalk

Oct 29, 2022 · Artificial Intelligence

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 NetworkKuaishouMachine Learning

0 likes · 14 min read

Uplift Modeling: Quantifying Heterogeneous Treatment Effects at Kuaishou

DataFunTalk

May 10, 2022 · Artificial Intelligence

Experimental Science and Causal Inference Forum – Sessions Overview at DataFun Summit 2022

The DataFun Summit 2022 features an Experimental Science and Causal Inference forum where leading data scientists from Didi, Tencent, Google, ByteDance, and others present deep technical talks on causal inference methods, A/B testing, game operations, and advertising experiments, offering practical insights and audience takeaways.

A/B testingData Scienceadvertising

0 likes · 10 min read

DataFunSummit

Mar 27, 2022 · Artificial Intelligence

Causal Machine Learning for User Growth: Concepts, Methods, and Applications

This article explores how combining causal inference with machine learning can uncover subtle correlations in large datasets, detailing user growth metrics, propensity‑score matching, causal recommendation models, heterogeneous treatment effect analysis, and practical strategies for improving retention and activity in recommendation systems.

Machine Learningcausal inferenceheterogeneous treatment effect

0 likes · 12 min read

Causal Machine Learning for User Growth: Concepts, Methods, and Applications

DataFunTalk

Feb 7, 2022 · Artificial Intelligence

Causal Machine Learning for User Growth: Concepts, Methods, and Applications

This article explores how combining causal inference with machine learning can detect subtle correlations in large datasets, improve user growth metrics such as retention and activity, and presents practical methods like propensity score matching, uplift modeling, HTE analysis, and meta‑learners applied to recommendation systems.

Machine Learningcausal inferenceheterogeneous treatment effect

0 likes · 13 min read