Installation

Installation with conda is recommended.

conda environment files for Python 3.7, 3.8 and 3.9 are available in the repository. To use models under the inference.tf module (e.g. DragonNet), additional dependency of tensorflow is required. For detailed instructions, see below.

Install using `conda`:

Install conda with:

wget https://repo.anaconda.com/miniconda/Miniconda3-py38_23.5.0-3-Linux-x86_64.sh
bash Miniconda3-py38_23.5.0-3-Linux-x86_64.sh -b
source miniconda3/bin/activate 
conda init
source ~/.bashrc 

Install from source:

Create a clean conda environment

conda create -n causalml-py38 -y python=3.8
conda activate causalml-py38
conda install -c conda-forge cxx-compiler
conda install python-graphviz
conda install -c conda-forge xorg-libxrender

Then:

git clone https://github.com/uber/causalml.git
cd causalml
pip install .
python setup.py build_ext --inplace

Quick Start

Average Treatment Effect Estimation with S, T, X, and R Learners

from causalml.inference.meta import LRSRegressor
from causalml.inference.meta import XGBTRegressor, MLPTRegressor
from causalml.inference.meta import BaseXRegressor
from causalml.inference.meta import BaseRRegressor
from xgboost import XGBRegressor
from causalml.dataset import synthetic_data

y, X, treatment, _, _, e = synthetic_data(mode=1, n=1000, p=5, sigma=1.0)

lr = LRSRegressor()
te, lb, ub = lr.estimate_ate(X, treatment, y)
print('Average Treatment Effect (Linear Regression): {:.2f} ({:.2f}, {:.2f})'.format(te[0], lb[0], ub[0]))

xg = XGBTRegressor(random_state=42)
te, lb, ub = xg.estimate_ate(X, treatment, y)
print('Average Treatment Effect (XGBoost): {:.2f} ({:.2f}, {:.2f})'.format(te[0], lb[0], ub[0]))

nn = MLPTRegressor(hidden_layer_sizes=(10, 10),
                 learning_rate_init=.1,
                 early_stopping=True,
                 random_state=42)
te, lb, ub = nn.estimate_ate(X, treatment, y)
print('Average Treatment Effect (Neural Network (MLP)): {:.2f} ({:.2f}, {:.2f})'.format(te[0], lb[0], ub[0]))

xl = BaseXRegressor(learner=XGBRegressor(random_state=42))
te, lb, ub = xl.estimate_ate(X, treatment, y, e)
print('Average Treatment Effect (BaseXRegressor using XGBoost): {:.2f} ({:.2f}, {:.2f})'.format(te[0], lb[0], ub[0]))

rl = BaseRRegressor(learner=XGBRegressor(random_state=42))
te, lb, ub =  rl.estimate_ate(X=X, p=e, treatment=treatment, y=y)
print('Average Treatment Effect (BaseRRegressor using XGBoost): {:.2f} ({:.2f}, {:.2f})'.format(te[0], lb[0], ub[0]))

See the Meta-learner example notebook for details.

Source of this post: uber/causalml