optimalgiv 0.1.1

Python ⇄ Julia bridge for the OptimalGIV package

optimalgiv

A minimal Python wrapper for OptimalGIV.jl, a Julia package developed by Zhiyu Fu for estimating Generalized Instrumental Variable (GIV) models.

This interface enables Python users to call GIV estimators directly on pandas DataFrames using JuliaCall. Julia is automatically installed and all dependencies are resolved without manual setup.

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Installation

pip install optimalgiv

On first use, optimalgiv will automatically:

• Install Julia (if not already available)
• Install OptimalGIV.jl and supporting packages
• Precompile and create a self-contained Julia environment

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Quickstart

import pandas as pd
import numpy as np
from optimalgiv import giv

df = pd.DataFrame({
    "id":  np.repeat([1, 2], 5),
    "t":   list(range(1, 6)) * 2,
    "q":   np.random.randn(10),
    "p":   np.random.randn(10),
    "η1":  np.random.randn(10),
    "η2":  np.random.randn(10),
    "absS": np.abs(np.random.randn(10)),
})

model = giv(
    df,
    "q + endog(p) ~ id & (η1 + η2)",
    id="id", t="t", weight="absS",
    algorithm="scalar_search",
    guess={"Aggregate": 2.0}
)

print(model.coef)
print(model.coefficient_table())

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Credits

This package wraps the core functionality of OptimalGIV.jl, authored by Zhiyu Fu. All modeling logic and algorithms originate from her original Julia implementation.

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License

MIT License