A causal tree method (Causal Tree Learn (CTL)) for heterogeneous treatment effects in Python
☆67Mar 7, 2024Updated 2 years ago
Alternatives and similar repositories for CTL
Users that are interested in CTL are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- Working repository for Causal Tree and extensions☆450Apr 12, 2025Updated last year
- Causal Inference☆11May 25, 2020Updated 6 years ago
- Intensive Python programming course materials for PUCP☆59Oct 24, 2026Updated last week
- Multiple Response Uplift (or heterogeneous treatment effects) package that builds and evaluates tradeoffs with multiple treatments and mu…☆71Apr 26, 2025Updated last year
- ☆35Sep 29, 2018Updated 8 years ago
- 1-Click AI Models by DigitalOcean Gradient • AdDeploy popular AI models on DigitalOcean Gradient GPU virtual machines with just a single click. Zero configuration with optimized deployments.
- (ICML2020) “Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models’’☆31Mar 24, 2023Updated 3 years ago
- ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Inte…☆4,835Updated this week
- ➕➕ Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.☆133Mar 24, 2023Updated 3 years ago
- A Causal Decision Tree algorithm for causality inference with continuous variables☆23Jan 15, 2021Updated 5 years ago
- ☆17Sep 17, 2019Updated 7 years ago
- ☆299Apr 3, 2022Updated 4 years ago
- A Python Framework for Automatically Evaluating various Uplift Modeling Algorithms to Estimate Individual Treatment Effects