Open-All-Scale-Causal-Engine / OpenASCE
OpenASCE (Open All-Scale Casual Engine) is a Python package for end-to-end large-scale causal learning. It provides causal discovery, causal effect estimation and attribution algorithms all in one package.
☆70Updated last year
Alternatives and similar repositories for OpenASCE:
Users that are interested in OpenASCE are comparing it to the libraries listed below
- YLearn, a pun of "learn why", is a python package for causal inference☆413Updated 6 months ago
- CausalMatch is a Bytedance research project aimed at integrating cutting-edge machine learning and econometrics methods to bring about au…☆51Updated last month
- 因果推理&AB实验相关论文小书库☆220Updated last year
- It is a high-performance causal inference (statistical model) computing library based on OLAP, which solves the performance bottleneck of…☆134Updated 2 months ago
- ☆33Updated 3 years ago
- 在常规推荐系统算法和系统双优化的范式下,一线公司针对单个任务或单个业务的效果挖掘几乎达到极限。从2019年我们开始关注多种信息的萃取融合,提出了OneRec算法,希望通过平台或外部各种各样的信息来进行知识集成,打破数据孤岛,极大扩充推荐的“Extra World Knowl…☆110Updated this week
- [ICLR 2021] Code for: Varying Coefficient Neural Network with Functional Targeted Regularization for Estimating Continuous Treatment Effe…☆72Updated 2 years ago
- Implementation of paper DESCN, which is accepted in SIGKDD 2022.☆76Updated last year
- Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.☆15Updated 5 years ago
- ☆39Updated 3 years ago
- 🛠 How to Apply Causal ML to Real Scene Modeling?How to learn Causal ML?【✔从Causal ML到实际场景的Uplift建模】☆124Updated last year
- An easy-to-use framework for large scale recommendation algorithms.☆90Updated this week
- Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing (KDD 2024)☆24Updated 9 months ago
- ☆105Updated last year
- ☆10Updated last year
- HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling☆280Updated 5 months ago
- The real-world Production Dataset from E-commerce platform Meituan☆21Updated 9 months ago
- ☆261Updated 2 years ago
- 把因果思维融入机器学习中☆79Updated 5 years ago
- We organize papers related to causal that published on top conferences recently. (因果领域论文分类汇总)☆77Updated last year
- Multiple Response Uplift (or heterogeneous treatment effects) package that builds and evaluates tradeoffs with multiple treatments and mu…☆66Updated 7 months ago
- Rankability Enhanced Revenue Uplift Modeling Framework for Online Marketing (KDD 2024)☆12Updated 9 months ago
- the baseline for NeurIPS_Auto_Bidding_AIGB_Track☆47Updated 6 months ago
- code for "Addressing Exposure Bias in Uplift Modeling forLarge-scale Online Advertising"☆29Updated 3 years ago
- 💉📈 Dose response networks (DRNets) are a method for learning to estimate individual dose-response curves for multiple parametric treatm…☆87Updated last year
- Ant Graph Learning (AGL) provides a comprehensive solution for graph learning tasks at an industrial scale.☆84Updated last year
- Causal Effect Engine is a Golang package for causal inference☆18Updated 3 years ago
- Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.☆74Updated 3 years ago
- KwaiRec: A Fully-observed Dataset for Recommender Systems.☆142Updated 2 months ago
- A causal tree method (Causal Tree Learn (CTL)) for heterogeneous treatment effects in Python☆62Updated last year