hypex 0.1.0

Fast and customizable framework for Causal Inference

HypEx: Advanced Causal Inference and AB Testing Toolkit

Introduction

HypEx (Hypotheses and Experiments) is a comprehensive library crafted to streamline the causal inference and AB testing processes in data analytics. Developed for efficiency and effectiveness, HypEx employs Rubin's Causal Model (RCM) for matching closely related pairs, ensuring equitable group comparisons when estimating treatment effects.

Boasting a fully automated pipeline, HypEx adeptly calculates the Average Treatment Effect (ATE), Average Treatment Effect on the Treated (ATT), and Average Treatment Effect on the Control (ATC). It offers a standardized interface for executing these estimations, providing insights into the impact of interventions across various population subgroups.

Beyond causal inference, HypEx is equipped with robust AB testing tools, including Difference-in-Differences ( Diff-in-Diff) and CUPED methods, to rigorously test hypotheses and validate experimental results.

Features

• Faiss KNN Matching: Utilizes Faiss for efficient and precise nearest neighbor searches, aligning with RCM for optimal pair matching.
• Data Filters: Built-in outlier and Spearman filters ensure data quality for matching.
• Result Validation: Offers multiple validation methods, including random treatment, feature, and subset validations.
• Data Tests: Incorporates SMD, KS, PSI, and Repeats tests to affirm the robustness of effect estimations.
• Automated Feature Selection: Employs LGBM feature selection to pinpoint the most impactful features for causal analysis.
• AB Testing Suite: Features a suite of AB testing tools for comprehensive hypothesis evaluation.
• Stratification support: Stratify groups for nuanced analysis
• Weights support: Empower your analysis by assigning custom weights to features, enhancing the matching precision to suit your specific research needs

Warnings

Some functions in HypEx can facilitate solving specific auxiliary tasks but cannot automate decisions on experiment design. Below, we will discuss features that are implemented in HypEx but do not automate the design of experiments.

Feature Selection

Feature selection models the significance of features for the accuracy of target approximation. However, it does not rule out the possibility of overlooked features, the complex impact of features on target description, or the significance of features from a business logic perspective. The algorithm will not function correctly if there are data leaks.

Points to consider when selecting features:

• Data leaks - these should not be present.
• Influence on treatment distribution - features should not affect the treatment distribution.
• The target should be describable by features.
• All features significantly affecting the target should be included.
• The business rationale of features.
• The feature selection function can be useful for addressing these tasks, but it does not solve them nor does it absolve the user of the responsibility for their selection, nor does it justify it.

Link to ReadTheDocs

Multitarget

Multitarget involves studying the impact on multiple targets.

The algorithm is implemented as a repetition of the same matching on the same feature space and samples, but with different targets. To ensure the algorithm's correct operation, it's necessary to guarantee the independence of the targets from each other.

The best solution would be to conduct several independent experiments, each with its own set of features for each target.

Link to ReadTheDocs

Random Treatment и Random Feature

Random Treatment algorithm randomly shuffles the actual treatment. It is expected that the treatment's effect on the target will be close to 0.

Random Feature adds a feature with random values. It is expected that adding a random feature will maintain the same impact of the treatment on the target.

These methods are not sufficiently accurate markers of a successful experiment.

Link to ReadTheDocs

Quick Start

Explore usage examples and tutorials here.

Installation

pip install hypex

Quick start

Matching example

from hypex import Matcher
from hypex.utils.tutorial_data_creation import create_test_data

# Define your data and parameters
df = create_test_data(rs=42, na_step=45, nan_cols=['age', 'gender'])

info_col = ['user_id']
outcome = 'post_spends'
treatment = 'treat'
model = Matcher(input_data=df, outcome=outcome, treatment=treatment, info_col=info_col)
results, quality_results, df_matched = model.estimate()

AA-test example

from hypex import AATest
from hypex.utils.tutorial_data_creation import create_test_data

data = create_test_data(rs=52, na_step=10, nan_cols=['age', 'gender'])

info_cols = ['user_id', 'signup_month']
target = ['post_spends', 'pre_spends']

experiment = AATest(info_cols=info_cols, target_fields=target)
results = experiment.process(data, iterations=1000)
results.keys()

AB-test example

from hypex import ABTest
from hypex.utils.tutorial_data_creation import create_test_data

data = create_test_data(rs=52, na_step=10, nan_cols=['age', 'gender'])

model = ABTest()
results = model.execute(
    data=data,
    target_field='post_spends',
    target_field_before='pre_spends',
    group_field='group'
)

model.show_beautiful_result()

Documentation

For more detailed information about the library and its features, visit our documentation on ReadTheDocs.

You'll find comprehensive guides and tutorials that will help you get started with HypEx, as well as detailed API documentation for advanced use cases.

Community and Contributions

Join our vibrant community! For guidelines on contributing, reporting issues, or seeking support, please refer to our Contributing Guidelines.

Success Stories

Discover how HypEx is revolutionizing causal inference in various fields. Check out our featured article on Habr (ru).

Join Our Community

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Conclusion

HypEx stands as an indispensable resource for data analysts and researchers delving into causal inference and AB testing. With its automated capabilities, sophisticated matching techniques, and thorough validation procedures, HypEx is poised to unravel causal relationships in complex datasets with unprecedented speed and precision.