CausalEstimate 0.2.1

A library for estimates of causal effects.

CausalEstimate

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CausalEstimate is a Python tool designed to produce causal estimates from propensity scores. It provides functionalities for matching, weighting, and other causal inference techniques, helping researchers and data scientists derive meaningful insights from observational data.

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Features

• Propensity score matching and weighting
• Tools for average treatment effect (ATE) estimation
• Easy integration with pandas DataFrames
• Bootstrap standard error estimation

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Installation

To install the required dependencies, run:

pip install -r requirements.txt

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Usage

Example: Matching

Here is an example of how to use the matching functionality in a Jupyter notebook:

import numpy as np
import pandas as pd
from CausalEstimate.matching import match_optimal

# Simulate data
ps = np.array([0.3, 0.90, 0.5, 0.34, 0.351, 0.32, 0.35, 0.81, 0.79, 0.77, 0.90, 0.6, 0.52, 0.55])
treated = np.array([1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
ids = np.array([101, 102, 103, 103, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211])

df = pd.DataFrame({
    'PID': ids,
    'treatment': treated,
    'ps': ps
})

# Perform matching
result = match_optimal(df, n_controls=3, caliper=0.1)
print(result)

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Development

Running Tests

To run the unit tests, use the following command:

python -m unittest

Linting

To lint the code using flake8, run:

pip install flake8
flake8 CausalEstimate tests

Formatting

To format the code using black, run:

pip install black
black CausalEstimate tests

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License

This project is licensed under the MIT License.