| Name | causaltensor JSON |
| Version |
0.1.8
JSON |
| download |
| home_page | |
| Summary | Package for causal inference in panels |
| upload_time | 2023-11-06 05:36:06 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3 |
| license | MIT License Copyright (c) 2022 Tianyi Peng Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
causal
inference
panel
matrix
tensor
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# CausalTensor
CausalTensor is a python package for doing causal inference and policy evaluation using panel data.
## What is CausalTensor
CausalTensor is a suite of tools for addressing questions like "What is the impact of strategy X to outcome Y" given time-series data colleting from multiple units. Answering such questions has wide range of applications from econometrics, operations research, business analytics, polictical science, to healthcare. Please visit our [complete documentation](https://causaltensor.readthedocs.io/) for more information.
## Installing CausalTensor
CausalTensor is compatible with Python 3 or later and also depends on numpy. The simplest way to install CausalTensor and its dependencies is from PyPI with pip, Python's preferred package installer.
$ pip install causaltensor
Note that CausalTensor is an active project and routinely publishes new releases. In order to upgrade CausalTensor to the latest version, use pip as follows.
$ pip install -U causaltensor
## Using CausalTensor
We have implemented the following estimators including the traditional method Difference-in-Difference and recent proposed methods such as Synthetic Difference-in-Difference, Matrix Completion with Nuclear Norm Minimization, and De-biased Convex Panel Regression.
| Estimator | Reference |
| ----------- | ----------- |
| [Difference-in-Difference (DID)](https://en.wikipedia.org/wiki/Difference_in_differences) | [Implemented through two-way fixed effects regression.](http://web.mit.edu/insong/www/pdf/FEmatch-twoway.pdf) |
| [De-biased Convex Panel Regression (DC-PR)](https://arxiv.org/abs/2106.02780) | Vivek Farias, Andrew Li, and Tianyi Peng. "Learning treatment effects in panels with general intervention patterns." Advances in Neural Information Processing Systems 34 (2021): 14001-14013. |
| [Synthetic Difference-in-Difference (SDID)](https://arxiv.org/pdf/1812.09970.pdf) | Dmitry Arkhangelsky, Susan Athey, David A. Hirshberg, Guido W. Imbens, and Stefan Wager. "Synthetic difference-in-differences." American Economic Review 111, no. 12 (2021): 4088-4118. |
| [Matrix Completion with Nuclear Norm Minimization (MC-NNM)](https://arxiv.org/abs/1710.10251)| Susan Athey, Mohsen Bayati, Nikolay Doudchenko, Guido Imbens, and Khashayar Khosravi. "Matrix completion methods for causal panel data models." Journal of the American Statistical Association 116, no. 536 (2021): 1716-1730. |
Please visit our [documentation](https://causaltensor.readthedocs.io/) for the usage instructions. Or check the following simple demo as a tutorial:
- [Panel Data Example](https://colab.research.google.com/github/TianyiPeng/causaltensor/blob/main/tutorials/Panel%20Data%20Example.ipynb)
- [Panel Data with Multiple Treatments](https://colab.research.google.com/github/TianyiPeng/causaltensor/blob/main/tutorials/Panel_Regression_with_Multiple_Interventions.ipynb)
- [MC-NNM with covariates and missing data](https://colab.research.google.com/github/TianyiPeng/causaltensor/blob/main/tests/MCNNM_test.ipynb)
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"description": "# CausalTensor\n CausalTensor is a python package for doing causal inference and policy evaluation using panel data. \n\n\n## What is CausalTensor\nCausalTensor is a suite of tools for addressing questions like \"What is the impact of strategy X to outcome Y\" given time-series data colleting from multiple units. Answering such questions has wide range of applications from econometrics, operations research, business analytics, polictical science, to healthcare. Please visit our [complete documentation](https://causaltensor.readthedocs.io/) for more information. \n\n## Installing CausalTensor\nCausalTensor is compatible with Python 3 or later and also depends on numpy. The simplest way to install CausalTensor and its dependencies is from PyPI with pip, Python's preferred package installer.\n\n $ pip install causaltensor\n\nNote that CausalTensor is an active project and routinely publishes new releases. In order to upgrade CausalTensor to the latest version, use pip as follows.\n\n $ pip install -U causaltensor\n \n## Using CausalTensor\nWe have implemented the following estimators including the traditional method Difference-in-Difference and recent proposed methods such as Synthetic Difference-in-Difference, Matrix Completion with Nuclear Norm Minimization, and De-biased Convex Panel Regression. \n\n| Estimator | Reference |\n| ----------- | ----------- |\n| [Difference-in-Difference (DID)](https://en.wikipedia.org/wiki/Difference_in_differences) | [Implemented through two-way fixed effects regression.](http://web.mit.edu/insong/www/pdf/FEmatch-twoway.pdf) |\n| [De-biased Convex Panel Regression (DC-PR)](https://arxiv.org/abs/2106.02780) | Vivek Farias, Andrew Li, and Tianyi Peng. \"Learning treatment effects in panels with general intervention patterns.\" Advances in Neural Information Processing Systems 34 (2021): 14001-14013. |\n| [Synthetic Difference-in-Difference (SDID)](https://arxiv.org/pdf/1812.09970.pdf) | Dmitry Arkhangelsky, Susan Athey, David A. Hirshberg, Guido W. Imbens, and Stefan Wager. \"Synthetic difference-in-differences.\" American Economic Review 111, no. 12 (2021): 4088-4118. |\n| [Matrix Completion with Nuclear Norm Minimization (MC-NNM)](https://arxiv.org/abs/1710.10251)| Susan Athey, Mohsen Bayati, Nikolay Doudchenko, Guido Imbens, and Khashayar Khosravi. \"Matrix completion methods for causal panel data models.\" Journal of the American Statistical Association 116, no. 536 (2021): 1716-1730. |\n\nPlease visit our [documentation](https://causaltensor.readthedocs.io/) for the usage instructions. Or check the following simple demo as a tutorial:\n\n- [Panel Data Example](https://colab.research.google.com/github/TianyiPeng/causaltensor/blob/main/tutorials/Panel%20Data%20Example.ipynb)\n- [Panel Data with Multiple Treatments](https://colab.research.google.com/github/TianyiPeng/causaltensor/blob/main/tutorials/Panel_Regression_with_Multiple_Interventions.ipynb)\n- [MC-NNM with covariates and missing data](https://colab.research.google.com/github/TianyiPeng/causaltensor/blob/main/tests/MCNNM_test.ipynb)\n",
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