Causal Relationships, New Approaches Inspired by Information Theory

Student: Gilmutdinov Mikhail

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2018

Inferring causality is a hard task that can be useful in sciences, social studies and corporate business. Usually, to infer the fact that X causes Y we conduct a controlled experiment, where the test subjects are randomly affected by X, and the outcomes are analyzed. When we cannot have such a test, we need to work with raw data. We can make certain assumptions about the data and build mathematical models to explain the causality. But when we cannot, we need more sophisticated approaches. In this paper, I try to use machine learning and deep neural networks to solve the general task at hand without making prior assumptions and analyze a narrower subproblem of bidirectional causation.

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