criteo-research / large-scale-ITE-UM-benchmark
☆21Updated 3 years ago
Alternatives and similar repositories for large-scale-ITE-UM-benchmark
Users that are interested in large-scale-ITE-UM-benchmark are comparing it to the libraries listed below
Sorting:
- Multiple Response Uplift (or heterogeneous treatment effects) package that builds and evaluates tradeoffs with multiple treatments and mu…☆68Updated 2 weeks ago
- Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.☆137Updated 10 months ago
- Codebase for GANITE: Estimation of Individualized Treatment Effects using GANs - ICLR 2018☆61Updated 5 years ago
- ☆58Updated 3 years ago
- 💉📈 Dose response networks (DRNets) are a method for learning to estimate individual dose-response curves for multiple parametric treatm…☆88Updated 2 years ago
- ➕➕ Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.☆128Updated 2 years ago
- Data derived from the Linked Births and Deaths Data (LBIDD); simulated pairs of treatment assignment and outcomes; scoring code☆83Updated 6 years ago
- (ICML2020) “Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models’’☆31Updated 2 years ago
- Counterfactual Regression☆307Updated 2 years ago
- ☆268Updated 3 years ago
- EconML/CausalML KDD 2021 Tutorial☆161Updated last year
- ☆33Updated 2 years ago
- Implementation of Deep IV: A Flexible Approach for Counterfactual Prediction☆156Updated 3 years ago
- pytorch implementation of dragonnet☆41Updated 2 years ago
- A causal tree method (Causal Tree Learn (CTL)) for heterogeneous treatment effects in Python☆63Updated last year
- A version of scikit-learn that includes implementations of Wager & Athey and Scott Powers causal forests.☆22Updated 8 years ago
- A curated list of awesome work on causal inference, particularly in machine learning.☆103Updated 4 years ago
- Counterfactual Regression☆23Updated 8 years ago
- Implementation of Johansson, Fredrik D., Shalit, Uri, and Sontag, David. Learning representations for counterfactual inference - ICML, 20…☆12Updated 4 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】☆132Updated 2 years ago
- Code for TEDVAE, a VAE-based treatment effect estimation algorithm.☆25Updated 2 years ago
- [ICLR 2021] Code for: Varying Coefficient Neural Network with Functional Targeted Regularization for Estimating Continuous Treatment Effe…☆77Updated 2 years ago
- 💊 Comparing causality methods in a fair and just way.☆138Updated 5 years ago
- ☆17Updated 5 years ago
- ☆44Updated 6 years ago
- BITES: Balanced Individual Treatment Effect for Survival data☆18Updated last year
- Software and pre-processed data for "Using Embeddings to Correct for Unobserved Confounding in Networks"☆56Updated 2 years ago
- Realistic benchmark for different causal inference methods. The realism comes from fitting generative models to data with an assumed caus…☆74Updated 4 years ago
- Non-parametrics for Causal Inference☆45Updated 3 years ago
- Causality with machine learning, topic including causal represenation learning, causal reinforcement learning☆11Updated 4 years ago