Connecting Machine Learning to Causal Structure Learning with the Jacobian Matrix
Date
2021
Authors
Chen, Xiongren
Editors
Advisors
Shi, Javen
Pang, Guansong
Pang, Guansong
Journal Title
Journal ISSN
Volume Title
Type:
Thesis
Citation
Statement of Responsibility
Conference Name
Abstract
In this thesis, a novel approach is proposed to connect machine learning to causal
structure learning with the Jacobian matrix of neural networks w.r.t. input variables.
Causal learning distinguishing causes and effects is the way human understanding and
modeling the world. In the machine learning era, it also ensures that the model is
more interpretable and sufficiently robust. Due to the enormous cost of the traditional
intervention and randomized experimental methods, studies of causal learning have
focused on passive observational data which can generally be divided into static data
and time-series data. For different data types and different levels of causal modeling,
different machine learning techniques are applied to do causal modeling and the causal
structure can be read out by the Jacobian matrix. We focus on three aspects in this
thesis. Firstly, a novel framework of neural networks to causal structure learning on
static data under structural causal models assumptions is proposed and the results
of various experiments show our method has achieved state-of-the-art performance.
Secondly, we extend static data causal modeling to the highest level as the physical
system which is usually in terms of ordinary differential equations. Lastly, our Jacobianbased
causal modeling framework is applied to time series data with the ORE-RNN
technique and the results show that the success of temporal causal structure learning
in time series cases.
School/Discipline
School of Computer Science
Dissertation Note
Thesis (MPhil) -- University of Adelaide, School of Computer Science, 2021
Provenance
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