A Causal Inference library for Big Data.
Project description
Reina
About Reina
ReInA (Reasoning In AI) is a causal inference platform aimed at estimating heterogeneous treatment effects in observational data. There are various open-source projects that provide convenient causal inference methods, but the current out-of-box packages are limited to local memory for computation. Hence, this project integrates Apache Spark with various machine learning (ML) powered causal inference frameworks, enabling causal analysis on big-data.
Installation
$ pip install reina
Quick Start
import reina
# Read data from a distributed storage (e.g., Hadoop HDFS, AWS S3)
data = spark.read
.format("csv")
.load("your_data.csv")
# Set up necessary parameters (parameters will vary depending on the method used)
treatment = ['name_of_treatment']
outcome = 'name_of_outcome'
# Setup and fit model
causal_model = reina.iv.tsls(data=data, treatment=treatment, outcome=outcome)
causal_model.fit(data=data, treatment)
# Get heterogeneous treatment effect
cate, ate = causal_model.effect()
print(cate)
print(ate)
Please refer to example notebooks for more detailed toy demonstrations.
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