causal-toolkit-yamashita 0.1.1

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

causal-toolkit-yamashita

A Python package for causal inference methods, including:

• Randomized experiment analysis — average treatment effect (ATE) with confidence intervals and p-values
• Propensity score methods — inverse probability weighting (IPW) and doubly robust estimation
• Meta-learners — S-learner, T-learner, X-learner, and double machine learning for conditional average treatment effects (CATE)

Installation

Install the released package from PyPI:

pip install causal-toolkit-yamashita

Or install from source in editable mode for development:

git clone https://github.com/yamashann/causal-toolkit-yamashita.git
cd causal-toolkit-yamashita
uv pip install -e .

Usage

from causal_toolkit_yamashita import (
    calculate_ate_ci,
    calculate_ate_pvalue,
    ipw,
    doubly_robust,
    s_learner_discrete,
    t_learner_discrete,
    x_learner_discrete,
    double_ml_cate,
)

# Randomized experiment ATE with 95% CI
ate, ci_lower, ci_upper = calculate_ate_ci(data)

# IPW estimate of ATE
ate = ipw(df, ps_formula="x1 + x2", T="treatment", Y="outcome")

# Heterogeneous treatment effects with an X-learner
cate_df = x_learner_discrete(train, test, X=["x1", "x2"], T="t", y="y")

API

rct — Randomized experiments

Function	Description
calculate_ate_ci(data, alpha=0.05)	Returns (ate, ci_lower, ci_upper)
calculate_ate_pvalue(data)	Returns (ate, t_stat, p_value)

propensity — Propensity score methods

Function	Description
ipw(df, ps_formula, T, Y)	Inverse probability weighted ATE
doubly_robust(df, formula, T, Y)	Doubly robust ATE estimate

meta_learners — CATE estimation

Function	Description
s_learner_discrete(train, test, X, T, y)	Single-model learner; returns DataFrame with cate column
t_learner_discrete(train, test, X, T, y)	Two-model learner
x_learner_discrete(train, test, X, T, y)	Cross-fitted X-learner
double_ml_cate(train, test, X, T, y)	Double machine learning CATE

Running tests

uv run pytest

License

MIT