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https://github.com/amazon-science/azcausal

Causal Inference in Python
https://github.com/amazon-science/azcausal

causal-inference did panel sdid

Last synced: about 23 hours ago
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Causal Inference in Python

Host: GitHub
URL: https://github.com/amazon-science/azcausal
Owner: amazon-science
License: apache-2.0
Created: 2023-06-06T15:40:48.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2024-11-26T23:54:18.000Z (about 1 month ago)
Last Synced: 2025-01-02T02:12:36.095Z (8 days ago)
Topics: causal-inference, did, panel, sdid
Language: Python
Homepage:
Size: 5.47 MB
Stars: 40
Watchers: 3
Forks: 5
Open Issues: 0
Metadata Files:
- Readme: README.rst
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

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.