- #Installing neural style in bash shell for windows 10 install#
- #Installing neural style in bash shell for windows 10 code#
- #Installing neural style in bash shell for windows 10 download#
From a local copy of the repo, run python setup.py install.
#Installing neural style in bash shell for windows 10 code#
The code can also be installed as a python package ( catenets). Similarly, the experiments in Curth & vd Schaar (2021b) can be replicated using the code inĮxperiments.experiments_inductive_bias or from shell using python run_experiments_inductive_bias.py.
#Installing neural style in bash shell for windows 10 download#
To run the ihdp experiments, first download the IHDP-100 data files from and place them in a folder called 'data/'. n_repeats # number of experiments to run for each configuration, defaults to 10 (should be set to 100 for IHDP) file_name # base file name to write to, defaults to 'results' 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 setting # different simulation settings in synthetic experiments (can be 1-5) experiment # defaults to 'simulation', 'ihdp' will run ihdp experiments
#Installing neural style in bash shell for windows 10 install#
Virtual environment and run pip install -r requirements.txt #install requirements To do so from shell, clone the repo, create a new predict ( X )Īll experiments in Curth & vd Schaar (2021a) can be replicated using this repository the necessaryĬode is in experiments.experiments_AISTATS21. predict ( X, return_po = True ) # predict potential outcomes too # estimate CATE using SNet s = SNet ( penalty_orthogonal = 0.01 ) s. predict ( X ) # without potential outcomes cate_pred_t, po0_pred_t, po1_pred_t = t. All hyperparameters are documented in detail in the respective files in catenets.models folder.Įxample usage: from 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. predict(X) method, where predict returns CATE by default.
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. 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.
The jax implementations in were used in all papers listed additionally, pytorch versions of some models ( ) were contributed by Bogdan Cebere.
Meta-learners for CATE estimation, including two-step pseudo-outcome regression estimators (theĭR-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.
We also provide Neural Network (NN)-based instantiations of a number of so-called NN-based algorithms from existing literature (Shalit et al (2017), Shi et al (2019), Hassanpour OffsetNet as discussed in Curth & vd Schaar (2021b), and re-implement a number of We implement the SNet-class we introduce in Curth & vd Schaar (2021a), as well as FlexTENet and 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" (Curth et al, 2021). NeurIPS21 paper "On Inductive Biases for Heterogeneous Treatment Effect Estimation" (Curth & vd 'Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to LearningĪlgorithms' (Curth & vd Schaar, 2021a) as well as the follow up CATENets - Conditional Average Treatment Effect Estimation Using Neural NetworksĬode Author: Alicia Curth ( repo contains Jax-based, sklearn-style implementations of Neural Network-based ConditionalĪverage Treatment Effect (CATE) Estimators, which were used in the AISTATS21 paper
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