Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.
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Updated
Jul 8, 2021 - Python
Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.
This is the public repository of the code implementation for KCRL.
Treatment evaluation in presence of large number of covariates or treatment heterogeneity through Machine Learning methods
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Causal Machine Learning project analyzing and evaluating different Double ML models for estimating treatment effects in observational data.
Collection and implementation of a variety of machine learning code examples (notebooks and Python scripts) and projects.
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Comparing effectiveness of the most common causal machine learning methods across various treatment effect, model complexities, data dimensions and sample sizes.
Basic experimental set-up for the comparison of causal structure learning algorithms as shown in "Beware of the Simulated DAG".
Implementations of var-sortability, sortnregress, and chain-orientation as presented in the article "Beware of the Simulated DAG": https://arxiv.org/abs/2102.13647.
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A resource list for causality in statistics, data science and physics
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