causal-toolkit-pmathew 0.1.0

A Python package for causal inference methods including ATE estimation, propensity score methods, and meta-learners.

causal_toolkit_pmathew

causal_toolkit_pmathew packages the course implementations from Weeks 02 through 05 into an installable causal inference toolkit.

Package contents

• causal_toolkit_pmathew/rct.py: randomized controlled trial estimators from Week 02
• causal_toolkit_pmathew/propensity.py: inverse propensity weighting and doubly robust estimators from Week 03
• causal_toolkit_pmathew/meta_learners.py: S-, T-, X-learner, and double machine learning CATE estimators from Weeks 04 and 05
• tests/: pytest coverage for the required modules

Installation

git clone https://github.com/prinupmathew/causal_toolkit_pmathew.git
cd causal_toolkit_pmathew
uv pip install -e .
python -m uv pip install -e .

Run tests

uv run pytest
python -m uv run pytest

Run pytest module with coverage

python -m pytest tests/ -v --cov=causal_toolkit_pmathew --cov-report=term-missing

Usage

import pandas as pd

from causal_toolkit_pmathew import (
	calculate_ate_ci,
	doubly_robust,
	ipw,
	s_learner_discrete,
	t_learner_discrete,
)

trial_data = pd.DataFrame({
	"T": [1, 1, 0, 0],
	"Y": [11.2, 10.8, 8.9, 9.1],
})

ate, ci_lower, ci_upper = calculate_ate_ci(trial_data)

observational_data = pd.DataFrame({
	"x": [0.2, 0.7, -0.1, 1.3],
	"t": [1, 1, 0, 0],
	"y": [3.4, 2.8, 1.1, 1.6],
})

ipw_estimate = ipw(observational_data, "x", "t", "y")
dr_estimate = doubly_robust(observational_data, "x", "t", "y")

train = observational_data.rename(columns={"x": "x1"}).assign(x2=[1.0, 0.5, 0.1, -0.2])
test = train.copy()

s_cate = s_learner_discrete(train, test, ["x1", "x2"], "t", "y")
t_cate = t_learner_discrete(train, test, ["x1", "x2"], "t", "y")

API

• calculate_ate_ci(data, alpha=0.05): estimate the average treatment effect and a two-sided confidence interval from an RCT dataset with T and Y columns
• calculate_ate_pvalue(data): estimate the ATE, test statistic, and two-sided p-value for an RCT dataset with T and Y columns
• ipw(df, ps_formula, T, Y): compute an inverse propensity weighted ATE
• doubly_robust(df, formula, T, Y): compute a doubly robust ATE using propensity and outcome models
• s_learner_discrete(train, test, X, T, y): estimate CATE with a single outcome model
• t_learner_discrete(train, test, X, T, y): estimate CATE with separate treated and control models
• x_learner_discrete(train, test, X, T, y): estimate CATE with the X-learner procedure
• double_ml_cate(train, test, X, T, y): estimate CATE using residualized treatment and outcome models