amazon-science/azcausal

amazon-science/azcausal

Description: Causal Inference in Python

Language: Python

License: Apache-2.0

Stars: 46

Forks: 10

Open issues: 0

Created: 2023-06-06T15:40:48Z

Pushed: 2026-05-09T00:13:55Z

Default branch: main

Fork: no

Archived: no

README: azcausal: Causal Inference in Python ====================================================================

Causal inference is an important component of the experiment evaluation. We highly recommend to have a look at the open-source book: Causal Inference for The Brave and True _

Please find the software documentation here: https://amazon-science.github.io/azcausal/latest/

Currently, azcausal provides two well-known and widely used causal inference methods: Difference-in-Difference (DID) and Synthetic Difference-in-Difference (SDID). Moreover, error estimates via Placebo, Boostrap, or JackKnife are available.

.. _Installation:

Installation ********************************************************************************

To install the current release, please execute:

.. code:: bash

pip install git+https://github.com/amazon-science/azcausal.git

.. _Usage:

Usage ********************************************************************************

.. code:: python

from azcausal.core.error import JackKnife from azcausal.core.panel import CausalPanel from azcausal.data import CaliforniaProp99 from azcausal.estimators.panel.sdid import SDID from azcausal.util import to_panels

load an example data set with the columns Year, State, PacksPerCapita, treated.

df = CaliforniaProp99().df()

create the panel data from the frame and define the causal types

data = to_panels(df, 'Year', 'State', ['PacksPerCapita', 'treated']) ctypes = dict(outcome='PacksPerCapita', time='Year', unit='State', intervention='treated')

initialize the panel

panel = CausalPanel(data).setup(**ctypes)

initialize an estimator object, here synthetic difference in difference (sdid)

estimator = SDID()

run the estimator

result = estimator.fit(panel)

run the error validation method

estimator.error(result, JackKnife())

plot the results

estimator.plot(result)

print out information about the estimate

print(result.summary(title="CaliforniaProp99"))

.. code:: bash

╭──────────────────────────────────────────────────────────────────────────────╮ | CaliforniaProp99 | ├──────────────────────────────────────────────────────────────────────────────┤ | Panel | | Time Periods: 31 (19/12) total (pre/post) | | Units: 39 (38/1) total (contr/treat) | ├──────────────────────────────────────────────────────────────────────────────┤ | ATT | | Effect (±SE): -15.60 (±2.9161) | | Confidence Interval (95%): [-21.32 , -9.8884] (-) | | Observed: 60.35 | | Counter Factual: 75.95 | ├──────────────────────────────────────────────────────────────────────────────┤ | Percentage | | Effect (±SE): -20.54 (±3.8393) | | Confidence Interval (95%): [-28.07 , -13.02] (-) | | Observed: 79.46 | | Counter Factual: 100.00 | ├──────────────────────────────────────────────────────────────────────────────┤ | Cumulative | | Effect (±SE): -187.25 (±34.99) | | Confidence Interval (95%): [-255.83 , -118.66] (-) | | Observed: 724.20 | | Counter Factual: 911.45 | ╰──────────────────────────────────────────────────────────────────────────────╯

.. image:: docs/source/images/sdid.png

.. _Estimators:

Estimators ********************************************************************************

• Difference-in-Difference (DID): Simple implementation of the well-known Difference-in-Difference estimator.
• Synthetic Difference-in-Difference (SDID): Arkhangelsky, Dmitry Athey, Susan Hirshberg, David A. Imbens, Guido W. Wager, Stefan Synthetic Difference-in-Differences American Economic Review 111 12 4088-4118 2021 10.1257/aer.20190159 https://www.aeaweb.org/articles?id=10.1257/aer.20190159. Implementation based on https://synth-inference.github.io/synthdid/

.. _Contact:

Contact ********************************************************************************

Feel free to contact me if you have any questions:

| Julian Blank _ (blankjul [at] amazon.com) | Amazon.com | Applied Scientist, Amazon | 410 Terry Ave N, Seattle 98109, WA.