skmiscpy

Contains a few functions useful for data-analysis, causal inference etc.

Installation

pip install skmiscpy

Usage

So far, skmiscpy can be used to do a basic causal analysis. Here very simple examples are shown for demonstration purposes. Check Causal Analysis Workflow & Estimating ATE Using skmiscpy for better understanding.

import pandas as pd
from skmiscpy import compute_smd, plot_smd
from skmiscpy import plot_mirror_histogram

Draw a mirror histogram

data = pd.DataFrame({
    'treatment': [1, 1, 0, 0, 1, 0],
    'propensity_score': [2.0, 3.5, 3.0, 2.2, 2.2, 3.3]
})

plot_mirror_histogram(data=data, var='propensity_score', group='treatment')

# Draw a weighted mirror histogram
data_with_weights = pd.DataFrame({
    'treatment': [1, 1, 0, 0, 1, 0],
    'propensity_score': [2.0, 3.5, 3.0, 2.2, 2.2, 3.3],
    'weights': [1.0, 1.5, 2.0, 1.2, 1.1, 0.8]
})

plot_mirror_histogram(
    data=data_with_weights, var='propensity_score', group='treatment', weights='weights',
    xlabel='Propensity Score', ylabel='Weighted Count', title='Weighted Mirror Histogram'
)

Compute Standardized Mean Difference (SMD)

sample_df = pd.DataFrame({
    'age': np.random.randint(18, 66, size=100),
    'weight': np.round(np.random.uniform(120, 200, size=100), 1),
    'gender': np.random.choice(['male', 'female'], size=100),
    'race': np.random.choice(
        ['white', 'black', 'hispanic'],
        size=100, p=[0.4, 0.3, 0.3]
    ),
    'educ_level': np.random.choice(
        ['bachelor', 'master', 'doctorate'],
        size=100, p=[0.3, 0.4, 0.3]
    ),
    'ps_wts': np.round(np.random.uniform(0.1, 1.0, size=100), 2),
    'group': np.random.choice(['treated', 'control'], size=100),
    'date': pd.date_range(start='2024-01-01', periods=100, freq='D')
})

# 1. Basic usage with unadjusted SMD only:
compute_smd(sample_df, vars=['age', 'weight', 'gender'], group='group', estimand='ATE')

# 2. Including weights for adjusted SMD:
compute_smd(
    sample_df, 
    vars=['age', 'weight', 'gender'], 
    group='group', wt_var='ps_wts',
    estimand='ATE'
)

# 3. Including categorical variables for adjusted SMD:
compute_smd(
    sample_df,
    vars=['age', 'weight', 'gender'],
    group='group',
    wt_var='ps_wts',
    cat_vars=['race', 'educ_level'],
    estimand='ATE'
)

Create a love plot (point plot of SMD)

data = pd.DataFrame({
    'variables': ['age', 'bmi', 'blood_pressure'],
    'unadjusted_smd': [0.25, 0.4, 0.1],
    'adjusted_smd': [0.05, 0.2, 0.08]
})

plot_smd(data)

## Adding a reference line at 0.1
plot_smd(data, add_ref_line=True, ref_line_value=0.1)

## Customizing the Seaborn plot with additional keyword arguments
plot_smd(data, add_ref_line=True, ref_line_value=0.1, palette='coolwarm', markers=['o', 's'])

Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

skmiscpy was created by Shafayet Khan Shafee. It is licensed under the terms of the MIT license.

Credits

skmiscpy was created with cookiecutter and the py-pkgs-cookiecutter template.