formative-ds 0.2.1

A Python library for causal effect estimation.

formative

Python library for causal effect estimation. You declare your causal assumptions as a DAG before choosing an estimation method, making identification explicit rather than implicit.

Requirements

• Python 3.10+

Installation

pip install formative-ds

Docs

Comprehensive documentation is available at docs.getformative.dev.

Usage

from formative import DAG, OLSObservational

dag = DAG()
dag.assume("ability").causes("education", "income")
dag.assume("education").causes("income")

result = OLSObservational(
    dag,
    treatment="education",
    outcome="income"
).fit(df)

print(result.summary())

Confounders declared in the DAG are controlled for automatically. If a confounder is absent from the dataframe, an IdentificationError is raised before any estimation runs. See online documentation at docs.getformative.dev for more examples and details.

Local development

Requires uv.

git clone https://github.com/maxpagels/formative
cd formative
uv sync --dev

This creates a .venv, installs all dependencies, and installs the package in editable mode.

Releasing a new version

uvx bump-my-version bump patch   # 0.1.0 → 0.1.1 (bug fixes)
uvx bump-my-version bump minor   # 0.1.0 → 0.2.0 (new features)
uvx bump-my-version bump major   # 0.1.0 → 1.0.0 (breaking changes)
git push --follow-tags            # triggers publish to PyPI

Running tests

uv run pytest

Importing without installing

To use formative from a script outside this repo without installing it, either prepend the path at runtime:

import sys
sys.path.insert(0, "/path/to/formative")

from formative import DAG, OLSObservational

Or set PYTHONPATH before running:

PYTHONPATH=/path/to/formative python your_script.py