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Discovering Reliable Causal Rules
[article]
2020
arXiv
pre-print
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Moreover, to discover reliable causal rules from a sample, we propose a conservative and consistent estimator of the causal effect, and derive an efficient and exact algorithm that maximises the estimator ...
Extensive experiments on a variety of real-world datasets show that the proposed algorithm is efficient and discovers meaningful rules. ...
Discovering Rules Now that we have a reliable and consistent estimator of the causal effect, we turn to discovering rules that maximize this estimator. ...
arXiv:2009.02728v2
fatcat:55tf7htnfzgg3ogxvz5lbvfbmy
A Comparison of Association Rule Discovery and Bayesian Network Causal Inference Algorithms to Discover Relationships in Discrete Data
[chapter]
2000
Lecture Notes in Computer Science
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This work compares the effectiveness of causal inference algorithms with association rule induction for discovering patterns in discrete data. ...
However, causal inference algorithms discover more concise relationships between variables, namely, relations of direct cause. ...
As well, allocation of rules as the "head" or "body" of an association rule is also not reliable as an indicator of causal direction. confidence (B → H) = support(B ∪ H) / support(B). ...
doi:10.1007/3-540-45486-1_27
fatcat:licw7sezqvfavpjhzentnejoyq
Discovery of Causal Rules Using Partial Association
2012
2012 IEEE 12th International Conference on Data Mining
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reliability of discovered causal rules. ...
The results show that our method can effectively discover interesting causal rules in large databases. ...
These causal rules represent a small set of statistically reliable relationships that are likely to embed cause and effect relationships. ...
doi:10.1109/icdm.2012.36
dblp:conf/icdm/JinL0LSW12
fatcat:iajmki3nvfbjzfizn4g7xz3oky
Discovering causal rules in relational databases
1997
Applied Artificial Intelligence
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This article explores the combined application of inductive learning algorithms and causal inference techniques to the problem of discovering causal rules among the attributes of a relational database. ...
When the variables are discrete or have been discretized to test conditional independencies, supervised induction algorithms can be used to learn causal rules, that is, conditional statements in which ...
y, such that the type of driving license is causally related to the reliability of the driver or Find all rules with support x and confidence y that explain why the driver has a high reliability. ...
doi:10.1080/088395197118352
fatcat:tzl77j3gufbyxietl2h7lwiusm
Inferring Implicit Rules by Learning Explicit and Hidden Item Dependency
2017
IEEE Transactions on Systems, Man & Cybernetics. Systems
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Explicit relations have been substantially studied by rule mining-based approaches, including association rule mining and causal rule discovery. ...
IRRMiner is applied to make recommendations and shows that the identified implicit rules can increase recommendation reliability. ...
Association rule mining has recently been combined with a cohort study to discover causal association rules and has proven to be quite effective [33] . ...
doi:10.1109/tsmc.2017.2768547
fatcat:jeht4oslijhwdb4zvtduxqtqxi
Mining Causal Association Rules
2013
2013 IEEE 13th International Conference on Data Mining Workshops
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In this paper we study how to use an efficient association mining approach to discover potential causal rules in observational data. ...
Discovering causal relationships is the ultimate goal of many scientific explorations. ...
However, they are reliable relationships since each causal rule is tested by the cohort study in data. Most discovered causal rules (99%) are short and include one or two variables. ...
doi:10.1109/icdmw.2013.88
dblp:conf/icdm/LiL0LJS13
fatcat:lyowshorxzcbhepmyuh4n2dpvm
Data Analytics and Mining in Healthcare with Emphasis on Causal Relationship Mining
2019
International journal of recent technology and engineering
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The present models for causality have limitations in terms of scalability and reliability. The present study is targeted to study causal models for causal relationship mining. ...
This study tried to conclude with some proposals for causal relationship discovery which are efficient, reliable and scalable. ...
Causal association rules are small in number, powerful, popular, useful, and reliable data relationships because each of the causal rules is tested by the cohort study in data. ...
doi:10.35940/ijrte.d6492.118419
fatcat:zkzif7glbvawhmqualnehr7se4
A Review on Algorithms for Constraint-based Causal Discovery
[article]
2016
arXiv
pre-print
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2016 pre-print arXiv:1611.03977v1 fatcat:gejrgtsagvbwhmh2mzp6gjnom4
Causal discovery studies the problem of mining causal relationships between variables from data, which is of primary interest in science. ...
As a conclusion, some open problems in constraint-based causal discovery are outlined for future research. ...
Instead of discovering the whole causal structure, this approach only studies direct causal relationships around a given target variable. ...
arXiv:1611.03977v2
fatcat:ercpfkqssnabfgdc3ndd7bd3tu
Finding Temporal Relations: Causal Bayesian Networks vs. C4.5
[chapter]
2000
Lecture Notes in Computer Science
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In this paper we apply TETRAD, a program that uses Bayesian networks to discover causal rules, and C4.5, which creates decision trees, to the problem of discovering relations among a set of variables in ...
The rules in the domain are known, so we are able to assess the effectiveness of each method. The agent's sensings of its environment and its own actions are saved in data records over time. ...
TETRAD [9] is a well-known causality miner that uses Bayesian networks [3] to find causal relations. One example of the type of rules discovered by TETRAD is x → y, which means that x causes y. ...
doi:10.1007/3-540-39963-1_28
fatcat:6wy7kirutverlbanqipnxuhkcm
Heuristic Mining Revamped: An Interactive, Data-aware, and Conformance-aware Miner
2017
International Conference on Business Process Management
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visualized as described in literature, and (5) existing tools do not give reliable quality diagnostics for discovered models. ...
It is the first tool that visualizes models using the concise Causal Net (C-Net) notation. We provide a walk-through of the iDHM by applying it to a large event log with hospital billing information. ...
., the event payload) are not used for process discovery; (4) discovered Causal Nets (C-Nets) are not visualized as described in literature; and (5) existing tools do not give reliable quality diagnostics ...
dblp:conf/bpm/MannhardtLR17
fatcat:3ea4ooyh3jguzaxqdvkkvc4hlu
Evaluation of the SRA Tool Using Data Mining Techniques
2003
International Conference on Advanced Information Systems Engineering
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The System Reliability Analyser (SRA) tool automates the process by iteratively manipulating the BBN model. ...
Data mining techniques are employed in order to identify whether the initial assumptions embedded in the system reliability model are met by results from scenario-based testing. ...
In our case we employ association rules in order to identify causal associations in our model. ...
dblp:conf/caise/GregoriadesSK03
fatcat:h4qnxtd53jhehmfsyzejhvvu5m
From Observational Studies to Causal Rule Mining
2015
ACM Transactions on Intelligent Systems and Technology
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2015 pre-print arXiv:1508.03819v1 fatcat:r7gdzcafozfctl7vpsc2mc2pri
In this paper we propose the concept of causal rules (CRs) and develop an algorithm for mining CRs in large data sets. ...
Specifically, association rule mining can be used to deal with the high-dimensionality problem while observational studies can be utilised to eliminate non-causal associations. ...
the idea of cohort studies to obtain reliable causal rules based on the candidates. ...
doi:10.1145/2746410
fatcat:n3tlix2rijfihji6hes2ujrjs4
Mining Causal Relationships in Multidimensional Time Series
[chapter]
2010
Studies in Computational Intelligence
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The main feature of the proposed system is supporting discovery of causal relations based on automatically discovered recurring patterns in the input time series. ...
The results show that the combined system can provide causality graphs representing the underlying relations between the human's actions and robot behavior that cannot be recovered using standard causal ...
There are three categories of Bayesian Causal Network Induction algorithms that utilize different aspects of causality to discover causal rules. ...
doi:10.1007/978-3-642-04584-4_14
fatcat:6f4yettaazg6bpw2x3it7opnki
An Overview of Bayesian Network Applications in Uncertain Domains
2015
Journal of clean energy technologies
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rule mining and medical domain analysis. ...
Thus, the real application of BN can be observed in a broad range of domains such as image processing, decision making, system reliability estimation and PPDM (Privacy Preserving in Data Mining) in association ...
The purpose of modifying the largest size transaction(s) is to keep the minimum perturbation to avoid generating new rules, ghost rules and lost rules with reliability. ...
doi:10.7763/ijcte.2015.v7.996
fatcat:qlhgx3kuevenxlvn3q5uu5lubu
Mining Relationship between Triggering and Consequential Events in a Short Transaction Database
[chapter]
2002
Proceedings of the 2002 SIAM International Conference on Data Mining
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determined by the first phase, i.e., the phase of discovering one-triggering causality rules. ...
In this paper, we decompose the problem of mining causality rules into two phases, i.e., the phase of discovering one-triggering causality rules and the phase of generating multi-triggering causality rules ...
doi:10.1137/1.9781611972726.24
dblp:conf/sdm/LeeYC02
fatcat:jmlolqvdijevpd2jecxtdjm7na
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