crflynn / skgrf
scikit-learn compatible Python bindings for grf (generalized random forests) C++ random forest library
☆31Updated 2 years ago
Alternatives and similar repositories for skgrf:
Users that are interested in skgrf are comparing it to the libraries listed below
- Implements the Causal Forest algorithm formulated in Athey and Wager (2018).☆69Updated 4 years ago
- Policy learning via doubly robust empirical welfare maximization over trees☆78Updated 9 months ago
- Quasi-Oracle Estimation of Heterogeneous Treatment Effects☆107Updated 3 years ago
- DEPRECATED. See new generalized random forest package for up-to-date implementation.☆52Updated 7 years ago
- ☆93Updated last year
- ☆42Updated 3 years ago
- difference-in-differences in Python☆100Updated last year
- Replication files for Chernozhukov, Newey, Quintas-Martínez and Syrgkanis (2021) "RieszNet and ForestRiesz: Automatic Debiased Machine Le…☆14Updated 2 years ago
- Implementation of Deep IV: A Flexible Approach for Counterfactual Prediction☆156Updated 3 years ago
- Design of Simulations using WGAN☆49Updated 2 years ago
- A causal tree method (Causal Tree Learn (CTL)) for heterogeneous treatment effects in Python☆62Updated last year
- R and python implementations of Accelerated Bayesian Causal Forest.☆25Updated 8 months ago
- Code for Shopper, a probabilistic model of shopping baskets☆52Updated 4 years ago
- A version of scikit-learn that includes implementations of Wager & Athey and Scott Powers causal forests.☆22Updated 8 years ago
- Data derived from the Linked Births and Deaths Data (LBIDD); simulated pairs of treatment assignment and outcomes; scoring code☆82Updated 6 years ago
- ☆29Updated last year
- Bayesian Causal Forests☆42Updated 10 months ago
- Approximately balanced estimation of average treatment effects in high dimensions.☆34Updated 3 years ago
- Multiple Response Uplift (or heterogeneous treatment effects) package that builds and evaluates tradeoffs with multiple treatments and mu…☆66Updated 7 months ago
- Code associated with paper: Orthogonal Machine Learning for Demand Estimation: High-Dimensional Causal Inference in Dynamic Panels, Seme…☆24Updated last year
- Double Machine Learning for Multiple Treatments☆40Updated 4 years ago
- Prediction and inference procedures for synthetic control methods with multiple treated units and staggered adoption.☆32Updated last month
- Synthetic difference in differences for Python☆76Updated 11 months ago
- ☆92Updated 3 months ago
- A Penalized Synthetic Control Estimator for Disaggregated Data (JASA, 2021)☆35Updated 3 weeks ago
- CausalModel implements widely used casual inference methods as well as an interference based method proposed by our paper.☆15Updated 10 months ago
- Non-parametrics for Causal Inference☆43Updated 3 years ago
- pytorch implementation of BLP'95☆26Updated 5 years ago
- This repository provides R-code for the estimation of the conditional average treatment effect (CATE) using machine learning (ML) methods…☆33Updated last month
- Packages of Example Data for The Effect☆138Updated 4 months ago