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.

• 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.
• Feature Selection: Employs LGBM and Catboost 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

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.

Note: For Matching, it's recommended not to use more than 7 features as it might result in the curse of dimensionality, making the results unrepresentative.

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

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

These method is not sufficiently accurate marker of a successful experiment.

Link to ReadTheDocs

pip install -U hypex

Explore usage examples and tutorials here.

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()

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()

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()

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.

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

Habr (ru) - discover how HypEx is revolutionizing causal inference in various fields.
A/B testing seminar - Seminar in NoML about matching and A/B testing
Matching with HypEx: Simple Guide - Simple matching guide with explanation
Matching with HypEx: Grouping - Matching with grouping guide
HypEx vs Causal Inference and DoWhy - discover why HypEx is the best solution for causal inference
HypEx vs Causal Inference and DoWhy: part 2 - discover why HypEx is the best solution for causal inference

Visit this notebook ain Kaggle and estimate results by yourself.

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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.