Theehawau / Causality

Uses the concept of causality inference and causal graph to answers some basic questions on breast cancer data, and finally merges this concept with machine learning

Causality on breast cancer data

The purpose of this project is to

• Perform a causal inference task using Pearl’s framework
• Infer the causal graph from observational data and then validate the graph
• Merge machine learning with causal inference

on breast cancer dataset

This breast cancer data includes 569 examples of cancer biopsies, each with 32 features. One feature is an identification number, another is the cancer diagnosis and 30 are numeric-valued laboratory measurements. The diagnosis is coded as "M" to indicate malignant or "B" to indicate benign.

The other 30 numeric measurements comprise the mean, standard error and worst (i.e. largest) value for 10 different characteristics of the digitized cell nuclei, which are as follows:-

• Radius
• Texture
• Perimeter
• Area
• Smoothness
• Compactness
• Concavity
• Concave Points
• Symmetry
• Fractal dimension

References

If correlation doesn’t imply causation, then what does? Quickstart Notebook for using Causalgraphicalmodels python module:used to describe and manipulate Causal Graphical Models and Structural Causal Models.