CauseBox
Causal inference is a critical task in various fields such as healthcare,economics, marketing and education. Recently, there have beensignificant advances through the application of machine learningtechniques, especially deep neural networks. Unfortunately, to-datemany of the proposed methods are evaluated on different (data,software/hardware, hyperparameter) setups and consequently it isnearly impossible to compare the efficacy of the available methodsor reproduce results presented in original research manuscripts.In this paper, we propose a causal inference toolbox (CauseBox)that addresses the aforementioned problems. At the time of thewriting, the toolbox includes seven state of the art causal inferencemethods and two benchmark datasets. By providing convenientcommand-line and GUI-based interfaces, theCauseBoxtoolboxhelps researchers fairly compare the state of the art methods intheir chosen application context against benchmark datasets.
Install / Use
/learn @paras2612/CauseBoxREADME
CauseBox-A-Causal-Inference-Toolbox-for-BenchmarkingTreatment-Effect-Estimators-with-Machine-Learning-Methods
Causal inference is a critical task in various fields such as healthcare,economics, marketing and education. Recently, there have beensignificant advances through the application of machine learningtechniques, especially deep neural networks. Unfortunately, to-datemany of the proposed methods are evaluated on different (data,software/hardware, hyperparameter) setups and consequently it isnearly impossible to compare the efficacy of the available methodsor reproduce results presented in original research manuscripts.In this paper, we propose a causal inference toolbox (CauseBox)that addresses the aforementioned problems. At the time of thewriting, the toolbox includes seven state of the art causal inferencemethods and two benchmark datasets. By providing convenientcommand-line and GUI-based interfaces, theCauseBoxtoolboxhelps researchers fairly compare the state of the art methods intheir chosen application context against benchmark datasets.
Usage
- Uncompress datasets for IHDP before you use it as followings:
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In Windows, use the command <code>.DatasetScripts/IHDP_uncompress.bat</code>
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In Linux, use the command <code>.DatasetScripts/IHDP_uncompress.sh</code>
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Please download R(version==4.08) on the internet
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Run the GUI using the command: <code>python GUI_main.py</code>
