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do-calculus
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Causing: CAUsal INterpretation using Graphs
python
graphs
automatic-differentiation
pytorch
neural-networks
graph-theory
identification
derivatives
mediation-analysis
dag
causality-analysis
latent-variables
structural-equation-modeling
structural-analysis
effects-modeling
causal-networks
simultaneous-equation
gnn
do-calculus
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Aug 5, 2021 - Python
Use regression, inverse probability weighting, and matching to close confounding backdoors and find causation in observational data
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Feb 27, 2020
Summary of useful results in Causal Inference
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May 8, 2021 - TeX
A Python implementation of the do-calculus of Judea Pearl et al.
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Apr 19, 2021 - Python
Basic demonstration of causal effects for Pearl's do-calculus
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Jun 17, 2019 - Jupyter Notebook
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When you miss declaring a node in your causal graph, it's going to throw a
KeyError: 'label'error. It could be more explicit to make debugging easier. I think it would be nice to inform what is the node hough used in the graph.