167 research outputs found
A Survey on Causal Discovery Methods for Temporal and Non-Temporal Data
Causal Discovery (CD) is the process of identifying the cause-effect
relationships among the variables from data. Over the years, several methods
have been developed primarily based on the statistical properties of data to
uncover the underlying causal mechanism. In this study we introduce the common
terminologies in causal discovery, and provide a comprehensive discussion of
the approaches designed to identify the causal edges in different settings. We
further discuss some of the benchmark datasets available for evaluating the
performance of the causal discovery algorithms, available tools to perform
causal discovery readily, and the common metrics used to evaluate these
methods. Finally, we conclude by presenting the common challenges involved in
CD and also, discuss the applications of CD in multiple areas of interest
Inductive Biases for Deep Learning of Higher-Level Cognition
A fascinating hypothesis is that human and animal intelligence could be
explained by a few principles (rather than an encyclopedic list of heuristics).
If that hypothesis was correct, we could more easily both understand our own
intelligence and build intelligent machines. Just like in physics, the
principles themselves would not be sufficient to predict the behavior of
complex systems like brains, and substantial computation might be needed to
simulate human-like intelligence. This hypothesis would suggest that studying
the kind of inductive biases that humans and animals exploit could help both
clarify these principles and provide inspiration for AI research and
neuroscience theories. Deep learning already exploits several key inductive
biases, and this work considers a larger list, focusing on those which concern
mostly higher-level and sequential conscious processing. The objective of
clarifying these particular principles is that they could potentially help us
build AI systems benefiting from humans' abilities in terms of flexible
out-of-distribution and systematic generalization, which is currently an area
where a large gap exists between state-of-the-art machine learning and human
intelligence.Comment: This document contains a review of authors research as part of the
requirement of AG's predoctoral exam, an overview of the main contributions
of the authors few recent papers (co-authored with several other co-authors)
as well as a vision of proposed future researc
Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs
This work addresses the problem of automated covariate selection under
limited prior knowledge. Given an exposure-outcome pair {X,Y} and a variable
set Z of unknown causal structure, the Local Discovery by Partitioning (LDP)
algorithm partitions Z into subsets defined by their relation to {X,Y}. We
enumerate eight exhaustive and mutually exclusive partitions of any arbitrary Z
and leverage this taxonomy to differentiate confounders from other variable
types. LDP is motivated by valid adjustment set identification, but avoids the
pretreatment assumption commonly made by automated covariate selection methods.
We provide theoretical guarantees that LDP returns a valid adjustment set for
any Z that meets sufficient graphical conditions. Under stronger conditions, we
prove that partition labels are asymptotically correct. Total independence
tests is worst-case quadratic in |Z|, with sub-quadratic runtimes observed
empirically. We numerically validate our theoretical guarantees on synthetic
and semi-synthetic graphs. Adjustment sets from LDP yield less biased and more
precise average treatment effect estimates than baselines, with LDP
outperforming on confounder recall, test count, and runtime for valid
adjustment set discovery
Connecting Machine Learning to Causal Structure Learning with the Jacobian Matrix
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.Thesis (MPhil) -- University of Adelaide, School of Computer Science, 202
Causal Discovery for Relational Domains: Representation, Reasoning, and Learning
Many domains are currently experiencing the growing trend to record and analyze massive, observational data sets with increasing complexity. A commonly made claim is that these data sets hold potential to transform their corresponding domains by providing previously unknown or unexpected explanations and enabling informed decision-making. However, only knowledge of the underlying causal generative process, as opposed to knowledge of associational patterns, can support such tasks.
Most methods for traditional causal discovery—the development of algorithms that learn causal structure from observational data—are restricted to representations that require limiting assumptions on the form of the data. Causal discovery has almost exclusively been applied to directed graphical models of propositional data that assume a single type of entity with independence among instances. However, most real-world domains are characterized by systems that involve complex interactions among multiple types of entities. Many state-of-the-art methods in statistics and machine learning that address such complex systems focus on learning associational models, and they are oftentimes mistakenly interpreted as causal. The intersection between causal discovery and machine learning in complex systems is small.
The primary objective of this thesis is to extend causal discovery to such complex systems. Specifically, I formalize a relational representation and model that can express the causal and probabilistic dependencies among the attributes of interacting, heterogeneous entities. I show that the traditional method for reasoning about statistical independence from model structure fails to accurately derive conditional independence facts from relational models. I introduce a new theory—relational d-separation—and a novel, lifted representation—the abstract ground graph—that supports a sound, complete, and computationally efficient method for algorithmically deriving conditional independencies from probabilistic models of relational data. The abstract ground graph representation also presents causal implications that enable the detection of causal direction for bivariate relational dependencies without parametric assumptions. I leverage these implications and the theoretical framework of relational d-separation to develop a sound and complete algorithm—the relational causal discovery (RCD) algorithm—that learns causal structure from relational data
Discovering phase and causal dependencies on manufacturing processes
Discovering phase and causal dependencies on manufacturing processes. Keyword machine learning, causality, Industry 4.
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