Counterfactual Regression
☆25Jul 12, 2016Updated 9 years ago
Alternatives and similar repositories for cfrnet
Users that are interested in cfrnet are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- ☆59Mar 24, 2022Updated 4 years ago
- Counterfactual Regression☆319Dec 7, 2022Updated 3 years ago
- ➕➕ Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.☆132Mar 24, 2023Updated 3 years ago
- Non-parametrics for Causal Inference☆50Mar 17, 2022Updated 4 years ago
- ☆45Apr 24, 2021Updated 5 years ago
- Bare Metal GPUs on DigitalOcean Gradient AI • AdPurpose-built for serious AI teams training foundational models, running large-scale inference, and pushing the boundaries of what's possible.
- A General Causal Inference Framework by Encoding Generative Modeling☆74Jun 1, 2024Updated last year
- Code for NeurIPS 2020 paper: "Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks" by I. Bica…☆30Jan 8, 2021Updated 5 years ago
- ☆296Apr 3, 2022Updated 4 years ago
- Code for TEDVAE, a VAE-based treatment effect estimation algorithm.☆26Dec 6, 2022Updated 3 years ago
- 根据用户数据以及消费行为数据,使用Python建立分类模型,通过评估客户流失的风险来预测客户流转情况,找到对客户影响较大的因素,进而挽留客户☆10Sep 28, 2020Updated 5 years ago
- Some baselines for PCIC2021 Track 2: Causal Inference and Recommendation☆17May 29, 2023Updated 2 years ago
- ☆11Mar 23, 2024Updated 2 years ago
- SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks☆14Mar 2, 2023Updated 3 years ago
- ☆32Jul 8, 2018Updated 7 years ago
- Deploy on Railway without the complexity - Free Credits Offer • AdConnect your repo and Railway handles the rest with instant previews. Quickly provision container image services, databases, and storage volumes.
- [ICLR 2021] Code for: Varying Coefficient Neural Network with Functional Targeted Regularization for Estimating Continuous Treatment Effe…☆97Sep 11, 2022Updated 3 years ago
- ☆14Jul 5, 2023Updated 2 years ago
- Replication of the paper "Adaptive dropout for training deep neural networks" using Lasagne.☆12Sep 27, 2016Updated 9 years ago
- Causal inference for recommendation☆25Jan 19, 2019Updated 7 years ago
- Pytorch implementation of Adaptative Dropout a.ka Standout.☆12Feb 22, 2018Updated 8 years ago
- ☆44Feb 11, 2019Updated 7 years ago
- 大数据【企业级360°全方位用户画像】标签开发部分源码☆20Dec 18, 2020Updated 5 years ago
- Data derived from the Linked Births and Deaths Data (LBIDD); simulated pairs of treatment assignment and outcomes; scoring code☆85May 23, 2018Updated 8 years ago
- NeurIPS paper 'Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis'☆11Oct 28, 2022Updated 3 years ago
- Deploy on Railway without the complexity - Free Credits Offer • AdConnect your repo and Railway handles the rest with instant previews. Quickly provision container image services, databases, and storage volumes.
- Deep universal probabilistic programming with Python and PyTorch☆12Apr 1, 2020Updated 6 years ago
- A new repository for model serving examples using Docker, Git HOOKS, Celery and Flask☆11Apr 29, 2026Updated 3 weeks ago
- A PyTorch implementation of BatchBALD on the MNIST dataset☆13Sep 16, 2020Updated 5 years ago
- [Python] Comparison of empirical probability distributions. Integral probability metrics (e.g. Kantorovich metric). f-divergences (e.g. K…☆11Apr 20, 2023Updated 3 years ago
- A simple video recommender to youtube videos☆18Jul 6, 2023Updated 2 years ago
- Code to reproduce the paper "Do causal predictors generalize better to new domains?"☆16Feb 7, 2025Updated last year
- ☆15Jan 9, 2026Updated 4 months ago
- Multiple Response Uplift (or heterogeneous treatment effects) package that builds and evaluates tradeoffs with multiple treatments and mu…☆72Apr 26, 2025Updated last year
- Binary Classifier Calibration Models☆17Feb 27, 2017Updated 9 years ago
- Serverless GPU API endpoints on Runpod - Get Bonus Credits • AdSkip the infrastructure headaches. Auto-scaling, pay-as-you-go, no-ops approach lets you focus on innovating your application.
- This packages provides a simple python implementation of Invariant Causal Prediction (ICP)☆13Mar 22, 2024Updated 2 years ago
- ☆12Apr 17, 2024Updated 2 years ago
- ☆13Jul 13, 2022Updated 3 years ago
- 使用决策树进行客户流失预测分析☆12Jan 17, 2018Updated 8 years ago
- ☆36Aug 19, 2025Updated 9 months ago
- ☆15May 19, 2025Updated last year
- ☆16Apr 26, 2023Updated 3 years ago