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+# CATENets - Conditional Average Treatment Effect Estimation Using Neural Networks
+
+[![CATENets Tests](https://github.com/AliciaCurth/CATENets/actions/workflows/test.yml/badge.svg)](https://github.com/AliciaCurth/CATENets/actions/workflows/test.yml)
+[![Documentation Status](https://readthedocs.org/projects/catenets/badge/?version=latest)](https://catenets.readthedocs.io/en/latest/?badge=latest)
+[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://github.com/AliciaCurth/CATENets/blob/main/LICENSE)
+
+
+Code Author: Alicia Curth (amc253@cam.ac.uk)
+
+This repo contains Jax-based, sklearn-style implementations of Neural Network-based Conditional
+Average Treatment Effect (CATE) Estimators, which were used in the AISTATS21 paper
+['Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning
+Algorithms']( https://arxiv.org/abs/2101.10943) (Curth & vd Schaar, 2021a) as well as the follow up
+NeurIPS21 paper ["On Inductive Biases for Heterogeneous Treatment Effect Estimation"](https://arxiv.org/abs/2106.03765) (Curth & vd
+Schaar, 2021b) and the NeurIPS21 Datasets & Benchmarks track paper ["Really Doing Great at Estimating CATE? A Critical Look at ML Benchmarking Practices in Treatment Effect Estimation"](https://openreview.net/forum?id=FQLzQqGEAH) (Curth et al, 2021). 
+
+We implement the SNet-class we introduce in Curth & vd Schaar (2021a), as well as FlexTENet and
+OffsetNet as discussed in Curth & vd Schaar (2021b), and re-implement a number of
+NN-based algorithms from existing literature (Shalit et al (2017), Shi et al (2019), Hassanpour
+& Greiner (2020)). We also provide Neural Network (NN)-based instantiations of a number of so-called
+meta-learners for CATE estimation, including two-step pseudo-outcome regression estimators (the
+DR-learner (Kennedy, 2020) and single-robust propensity-weighted (PW) and regression-adjusted (RA) learners), Nie & Wager (2017)'s R-learner and Kuenzel et al (2019)'s X-learner. The jax implementations in ``catenets.models.jax`` were used in all papers listed; additionally, pytorch versions of some models (``catenets.models.torch``) were contributed by [Bogdan Cebere](https://github.com/bcebere).
+
+### Interface
+The repo contains a package ``catenets``, which contains all general code used for modeling and evaluation, and a folder ``experiments``, in which the code for replicating experimental results is contained. All implemented learning algorithms in ``catenets`` (``SNet, FlexTENet, OffsetNet, TNet, SNet1 (TARNet), SNet2
+(DragonNet), SNet3, DRNet, RANet, PWNet, RNet, XNet``) come with a sklearn-style wrapper,  implementing a ``.fit(X, y, w)`` and a ``.predict(X)`` method, where predict returns CATE by default. All hyperparameters are documented in detail in the respective files in ``catenets.models`` folder.
+
+Example usage:
+
+```python
+from catenets.models.jax import TNet, SNet
+from catenets.experiment_utils.simulation_utils import simulate_treatment_setup
+
+# simulate some data (here: unconfounded, 10 prognostic variables and 5 predictive variables)
+X, y, w, p, cate = simulate_treatment_setup(n=2000, n_o=10, n_t=5, n_c=0)
+
+# estimate CATE using TNet
+t = TNet()
+t.fit(X, y, w)
+cate_pred_t = t.predict(X)  # without potential outcomes
+cate_pred_t, po0_pred_t, po1_pred_t = t.predict(X, return_po=True)  # predict potential outcomes too
+
+# estimate CATE using SNet
+s = SNet(penalty_orthogonal=0.01)
+s.fit(X, y, w)
+cate_pred_s = s.predict(X)
+
+```
+
+All experiments in Curth & vd Schaar (2021a) can be replicated using this repository; the necessary
+code is in ``experiments.experiments_AISTATS21``. To do so from shell, clone the repo, create a new
+virtual environment and run
+```shell
+pip install -r requirements.txt #install requirements
+python run_experiments_AISTATS.py
+```
+```shell
+Options:
+--experiment # defaults to 'simulation', 'ihdp' will run ihdp experiments
+--setting # different simulation settings in synthetic experiments (can be 1-5)
+--models # defaults to None which will train all models considered in paper,
+         # can be string of model name (e.g 'TNet'), 'plug' for all plugin models,
+         # 'pseudo' for all pseudo-outcome regression models
+
+--file_name # base file name to write to, defaults to 'results'
+--n_repeats # number of experiments to run for each configuration, defaults to 10 (should be set to 100 for IHDP)
+```
+
+Similarly, the experiments in Curth & vd Schaar (2021b) can be replicated using the code in
+``experiments.experiments_inductivebias_NeurIPS21`` (or from shell using ```python
+run_experiments_inductive_bias_NeurIPS.py```) and the experiments in Curth et al (2021) can be replicated using the code in ``experiments.experiments_benchmarks_NeurIPS21`` (the catenets experiments can also be run from shell using ``python run_experiments_benchmarks_NeurIPS``).
+
+The code can also be installed as a python package (``catenets``). From a local copy of the repo, run ``python setup.py install``.
+
+Note: jax is currently only supported on macOS and linux, but can be run from windows using WSL (the windows subsystem for linux).
+
+
+### Citing
+
+If you use this software please cite the corresponding paper(s):
+
+```
+@inproceedings{curth2021nonparametric,
+  title={Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms},
+  author={Curth, Alicia and van der Schaar, Mihaela},
+    year={2021},
+  booktitle={Proceedings of the 24th International Conference on Artificial
+  Intelligence and Statistics (AISTATS)},
+  organization={PMLR}
+}
+
+@article{curth2021inductive,
+  title={On Inductive Biases for Heterogeneous Treatment Effect Estimation},
+  author={Curth, Alicia and van der Schaar, Mihaela},
+  booktitle={Proceedings of the Thirty-Fifth Conference on Neural Information Processing Systems},
+  year={2021}
+}
+
+
+@article{curth2021really,
+  title={Really Doing Great at Estimating CATE? A Critical Look at ML Benchmarking Practices in Treatment Effect Estimation},
+  author={Curth, Alicia and Svensson, David and Weatherall, James and van der Schaar, Mihaela},
+  booktitle={Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks},
+  year={2021}
+}
+
+```