1,050 research outputs found
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Temporal and Relational Models for Causality: Representation and Learning
Discovering causal dependence is central to understanding the behavior of complex systems and to selecting actions that will achieve particular outcomes. The majority of work in this area has focused on propositional domains, where data instances are assumed to be independent and identically distributed (i.i.d.). However, many real-world domains are inherently relational, i.e., they consist of multiple types of entities that interact with each other, and temporal, i.e., they change over time. This thesis focuses on causal modeling for these more complex relational and temporal domains. This thesis provides an in-depth investigation of the properties of relational models and is extending their expressivity to include a temporal dimension. Specifically, we first investigate alternative ways to ground relational models, and we provide an in-depth analysis of the impact of alternative grounding semantics for feature construction, causal effect estimation, and model selection. Then, we extend relational models to represent discrete time. We generalize the theory of d-separation for this class of temporal and relational models. Finally, we provide a constraint-based algorithm, TRCD, to learn the structure of temporal relational models from data
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Method for Enabling Causal Inference in Relational Domains
The analysis of data from complex systems is quickly becoming a fundamental aspect of modern business, government, and science. The field of causal learning is concerned with developing a set of statistical methods that allow practitioners make inferences about unseen interventions. This field has seen significant advances in recent years. However, the vast majority of this work assumes that data instances are independent, whereas many systems are best described in terms of interconnected instances, i.e. relational systems. This discrepancy prevents causal inference techniques from being reliably applied in many real-world settings. In this thesis, I will present three contributions to the field of causal inference that seek to enable the analysis of relational systems. First, I will present theory for consistently testing statistical dependence in relational domains. I then show how the significance of this test can be measured in practice using a novel bootstrap method for structured domains. Second, I show that statistical dependence in relational domains is inherently asymmetric, implying a simple test of causal direction from observational data. This test requires no assumptions on either the marginal distributions of variables or the functional form of dependence. Third, I describe relational causal adjustment, a procedure to identify the effects of arbitrary interventions from observational relational data via an extension of Pearl\u27s backdoor criterion. A series of evaluations on synthetic domains shows the estimates obtained by relational causal adjustment are close to those obtained from explicit experimentation
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
Reasoning about Independence in Probabilistic Models of Relational Data
We extend the theory of d-separation to cases in which data instances are not
independent and identically distributed. We show that applying the rules of
d-separation directly to the structure of probabilistic models of relational
data inaccurately infers conditional independence. We introduce relational
d-separation, a theory for deriving conditional independence facts from
relational models. We provide a new representation, the abstract ground graph,
that enables a sound, complete, and computationally efficient method for
answering d-separation queries about relational models, and we present
empirical results that demonstrate effectiveness.Comment: 61 pages, substantial revisions to formalisms, theory, and related
wor
D'ya like DAGs? A Survey on Structure Learning and Causal Discovery
Causal reasoning is a crucial part of science and human intelligence. In
order to discover causal relationships from data, we need structure discovery
methods. We provide a review of background theory and a survey of methods for
structure discovery. We primarily focus on modern, continuous optimization
methods, and provide reference to further resources such as benchmark datasets
and software packages. Finally, we discuss the assumptive leap required to take
us from structure to causality.Comment: 35 page
Inferring Causality from Time Series data based on Structural Causal Model and its application to Neural Connectomics
Inferring causation from time series data is of scientific interest in
different disciplines, particularly in neural connectomics. While different
approaches exist in the literature with parametric modeling assumptions, we
focus on a non-parametric model for time series satisfying a Markovian
structural causal model with stationary distribution and without concurrent
effects. We show that the model structure can be used to its advantage to
obtain an elegant algorithm for causal inference from time series based on
conditional dependence tests, coined Causal Inference in Time Series (CITS)
algorithm. We describe Pearson's partial correlation and Hilbert-Schmidt
criterion as candidates for such conditional dependence tests that can be used
in CITS for the Gaussian and non-Gaussian settings, respectively. We prove the
mathematical guarantee of the CITS algorithm in recovering the true causal
graph, under standard mixing conditions on the underlying time series. We also
conduct a comparative evaluation of performance of CITS with other existing
methodologies in simulated datasets. We then describe the utlity of the
methodology in neural connectomics -- in inferring causal functional
connectivity from time series of neural activity, and demonstrate its
application to a real neurobiological dataset of electro-physiological
recordings from the mouse visual cortex recorded by Neuropixel probes
Causal Discovery from Temporal Data: An Overview and New Perspectives
Temporal data, representing chronological observations of complex systems,
has always been a typical data structure that can be widely generated by many
domains, such as industry, medicine and finance. Analyzing this type of data is
extremely valuable for various applications. Thus, different temporal data
analysis tasks, eg, classification, clustering and prediction, have been
proposed in the past decades. Among them, causal discovery, learning the causal
relations from temporal data, is considered an interesting yet critical task
and has attracted much research attention. Existing casual discovery works can
be divided into two highly correlated categories according to whether the
temporal data is calibrated, ie, multivariate time series casual discovery, and
event sequence casual discovery. However, most previous surveys are only
focused on the time series casual discovery and ignore the second category. In
this paper, we specify the correlation between the two categories and provide a
systematical overview of existing solutions. Furthermore, we provide public
datasets, evaluation metrics and new perspectives for temporal data casual
discovery.Comment: 52 pages, 6 figure
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