experiment-utils-pd 0.1.3

Set of utility functions for analyzing experimental and observational data

Experiment utils

Generic functions for experiment analysis and design:

• Experiment utils
• Installation
  • PyPI
  • From GitHub
• How to use it
  • Experiment Analyzer
  • Power Analysis

Installation

PyPI

pip install experiment-utils-pd

From GitHub

pip install git+https://github.com/sdaza/experiment-utils-pd.git

How to use it

Experiment Analyzer

df is a Pandas DataFrame:

from experiment_utils import ExperimentAnalyzer

# Example with balance adjustment and balance_method
analyzer = ExperimentAnalyzer(
    df,
    treatment_col="treatment",
    outcomes=['registrations', 'visits'],
    covariates=covariates,
    experiment_identifier=["campaign_key"],
    adjustment="balance",  # Options: 'balance', 'IV', or None
    balance_method="ps-logistic",  # Options: 'ps-logistic', 'ps-xgboost', 'entropy'
    target_effect="ATT"  # Options: 'ATT', 'ATE', 'ATC'
)

analyzer.get_effects()
print(analyzer.results)

Parameters:

• adjustment: Choose 'balance' for covariate balancing (using balance_method), 'IV' for instrumental variable adjustment, or None for unadjusted analysis.
• balance_method: Selects the method for balancing: 'ps-logistic' (logistic regression), 'ps-xgboost' (XGBoost), or 'entropy' (entropy balancing).
• target_effect: Specifies the estimand: 'ATT', 'ATE', or 'ATC'.

Power Analysis

from experiment_utils import PowerSim
p = PowerSim(metric='proportion', relative_effect=False,
  variants=1, nsim=1000, alpha=0.05, alternative='two-tailed')

p.get_power(baseline=[0.33], effect=[0.03], sample_size=[3000])