Predict whether the cancer is benign or malignant
When it comes to getting business insights from tabular data, the most interesting questions (from their perspective) are often not answerable with observational data alone! Judea Pearl and his research group have developed in the last decades a solid theoretical framework to deal with that, but the first steps toward merging it with mainstream machine learning are just beginning.
The causal graph is a central object in the framework mentioned above, but it is often unknown, subject to personal knowledge and bias, or loosely connected to the available data.
In this task we will highlight the importance of the matter in a concrete way, we attempt to do the following tasks:
- 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;
You can extract the data from kaggle or from UCI Machine Learning Repository.