Causal Discovery and Generative Neural Networks to Identify the Functional Causal Model

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Rajabi, Fatemeh | 2021

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 53840 (02)
University: Sharif University of Technology
Department: Mathematical Sciences
Advisor(s): Bahraini, Alireza
Abstract:
Causal discovery is of utmost importance for agents who must plan and decide based on observations. Since mistaking correlation with causation might lead to un- wanted consequences. The gold standard to discover causal relation is to perform experiments. However, experiments are in many cases expensive, unethical or impossible to perform. In these situations, there is a need for observational causal discovery. Causal discovery in the observational data setting involves making significant assumptions on the data and on the underlying causal model. This thesis aims to alleviate some of the assumptions and tries to identify the causal relationships and causal mechanisms using generative neural networks
Keywords:
Causal Inference ; Machine Learning ; Generative Networks ; Maximum Mean Discrepency ; Generative Neural Networks