Design and Implementation of a Distributed Causal Forest Framework on Meituan's Fulfillment Platform

Meituan’s Fulfillment Platform team built a high‑performance distributed causal‑forest framework—named Causal On Spark—that trains hundreds of trees on hundreds of millions of samples within minutes using MapReduce‑based histogram splitting, extensive memory optimizations, Parquet model serving, and novel distributed evaluation metrics, enabling scalable causal inference for pricing, subsidies, and marketing.

Model ServingSparkcausal forest

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Design and Implementation of a Distributed Causal Forest Framework on Meituan's Fulfillment Platform

DataFunSummit

Dec 26, 2023 · Artificial Intelligence

Applying Causal Inference Tools for Growth Scenarios in Industry

This article explains why causal inference tools are essential for industrial growth, outlines data‑flow standards such as randomized controlled trials, discusses model selection including causal forests and policy learning, and describes evaluation, offline simulation, and resource‑constrained optimization for deploying causal models in production.

AIModel Selectioncausal forest

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Applying Causal Inference Tools for Growth Scenarios in Industry

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

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