Description:

From multivariate data detecting which variable is the cause and which is effect is a difficult task. Finding right causation can be programmer's nightmare. Consider following case

Inputs:

1. If the grass is wet, then it rained
2. If we break this bottle, the grass will get wet

Program's inference: If we break this bottle, then it rained!

Co-occurrence does not imply cause and effect. From data how do we detect which variable is cause and which is effect? We propose novel markers like simple scatter plot of correlations and some color coded tests to identify causal pathways from multivariate data. We used python simulations to generate data of desired causal pathways. Altair visualizations helped verify our claim of casual markers.

In this talk I will walk you through various tests designed to detect possible casual pathway.

Prerequisites:

0. No python programming knowledge needed
1. High school mathematics
2. Simple statistics like, correlation, linear regression

Content URLs:

• github-code
• presentation

Speaker Info:

Vikrant Patil is a trainer with Pipal Academy. He delivers corporate trainings in basic python and advanced python. He is chief developer for open source energy modelling platform by prayas energy group, Pune. He has over 20 years of experience in software industry.

Speaker Links:

• github
• pycon-india-2019-talk