AliciaCurth / CATENets
Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.
β136Updated 8 months ago
Alternatives and similar repositories for CATENets:
Users that are interested in CATENets are comparing it to the libraries listed below
- ππ Dose response networks (DRNets) are a method for learning to estimate individual dose-response curves for multiple parametric treatmβ¦β87Updated last year
- β262Updated 2 years ago
- Counterfactual Regressionβ304Updated 2 years ago
- Data derived from the Linked Births and Deaths Data (LBIDD); simulated pairs of treatment assignment and outcomes; scoring codeβ82Updated 6 years ago
- ββ Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.β126Updated last year
- Codebase for GANITE: Estimation of Individualized Treatment Effects using GANs - ICLR 2018β58Updated 4 years ago
- Extensive tutorials for learning how to build deep learning models for causal inference (HTE) using selection on observables in Tensorfloβ¦β314Updated 5 months ago
- Realistic benchmark for different causal inference methods. The realism comes from fitting generative models to data with an assumed causβ¦β72Updated 3 years ago
- Non-parametrics for Causal Inferenceβ43Updated 3 years ago
- β58Updated 2 years ago
- Causal Effect Inference with Deep Latent-Variable Modelsβ334Updated 4 years ago
- Python implementation of the GES algorithm for causal discovery, from the 2002 paper "Optimal Structure Identification With Greedy Searchβ¦β55Updated 2 weeks ago
- Code for NeurIPS 2020 paper: "Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks" by I. Bicaβ¦β29Updated 4 years ago
- Code for "Learning End-to-End Patient Representations through Self-Supervised Covariate Balancing for Causal Treatment Effect Estimation"β22Updated last year
- [ICLR 2021] Code for: Varying Coefficient Neural Network with Functional Targeted Regularization for Estimating Continuous Treatment Effeβ¦β73Updated 2 years ago
- Example causal datasets with consistent formatting and ground truthβ80Updated last year
- β32Updated 2 years ago
- Makes algorithms/code in Tetrad available in Python via JPypeβ71Updated this week
- Implementation of Deep IV: A Flexible Approach for Counterfactual Predictionβ156Updated 3 years ago
- β43Updated 6 years ago
- BITES: Balanced Individual Treatment Effect for Survival dataβ18Updated last year
- Counterfactual Regressionβ23Updated 8 years ago
- Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.β74Updated 3 years ago
- EconML/CausalML KDD 2021 Tutorialβ161Updated last year
- Code for ICLR 2020 paper: "Estimating counterfactual treatment outcomes over time through adversarially balanced representations" by I. Bβ¦β58Updated 11 months ago
- A data index for learning causality.β461Updated last year
- A Python 3 package for learning Bayesian Networks (DAGs) from data. Official implementation of the paper "DAGMA: Learning DAGs via M-matrβ¦β114Updated last year
- A causal tree method (Causal Tree Learn (CTL)) for heterogeneous treatment effects in Pythonβ62Updated last year
- β204Updated last year
- Implementation of "DAGs with NO TEARS: Smooth Optimization for Structure Learning", by Zheng et al. (2018)β50Updated 5 years ago