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The Causal Graph Revisited for Directed Model Checking [chapter]

Martin Wehrle, Malte Helmert
2009 Lecture Notes in Computer Science  
In this paper, we introduce the causal graph structure to the context of directed model checking.  ...  Based on causal graph analysis, we first adapt a distance estimation function from AI planning to directed model checking.  ...  Acknowledgments This work was partly supported by the German Research Foundation (DFG) as part of the Transregional Collaborative Research Center "Automatic Verification and Analysis of Complex Systems  ... 
doi:10.1007/978-3-642-03237-0_8 fatcat:p5hmaillkzcxvfgrlr6wmxjmta

A Complexity Assessment for Queries Involving Sufficient and Necessary Causes [chapter]

Pedro Cabalar, Jorge Fandiño, Michael Fink
2014 Lecture Notes in Computer Science  
In this work, we revisit a recently proposed multi-valued semantics for logic programs where each true atom in a stable model is associated with a set of expressions (or causal justifications) involving  ...  Unfortunately, in the worst case, the number of causal justifications for an atom can be exponential with respect to the program size, so that computing the complete causal model may become intractable  ...  for more details), we can build the reduct P J using each non-causal stable model J and then proceed to compute its least causal model iterating the direct consequences operator for that reduct, T P J  ... 
doi:10.1007/978-3-319-11558-0_21 fatcat:k6qtsyz4gbgmlh7hct4r4uvoby

Robustness of Model Predictions under Extension [article]

Tineke Blom, Joris M. Mooij
2020 arXiv   pre-print
A caveat to using models for analysis is that predicted causal effects and conditional independences may not be robust under model extensions, and therefore applicability of such models is limited.  ...  We show how to use the technique of causal ordering to efficiently assess the robustness of qualitative model predictions and characterize a large class of model extensions that preserve these predictions  ...  Acknowledgments and Disclosure of Funding We thank Johannes Textor for interesting discussions about the viral infection model.  ... 
arXiv:2012.04723v1 fatcat:7qvbvk7n6rfrldwwqnt2kv4yf4

Tracing Causality and Co-movement between Pakistani and the Leading Foreign Stock Markets: A Graph Theoretic Approach

2020 International Review of Management and Business Research  
This paper developed and modify Peter and Clark (PC) causality algorithm to revisit the causal linkages between Pakistan and the leading foreign stock markets.  ...  Later on, (Swanson & Granger, 1997) for the first time used VAR residuals in PC algorithm to determine the causal ordering in time series.  ...  The results of GARCH and GJR model indicates that the return of Indian stock market (BSE-SENSEX) effect the return of Pakistan stock market (KSE-100) while the other two stock markets Bangladesh and Sir  ... 
doi:10.30543/9-4(2020)-37 fatcat:3i6qxq4iovatro6s6p2rtbgz3i

The potential dangers of causal consistency and an explicit solution

Peter Bailis, Alan Fekete, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica
2012 Proceedings of the Third ACM Symposium on Cloud Computing - SoCC '12  
Causal consistency is the strongest consistency model that is available in the presence of partitions and provides useful semantics for human-facing distributed services.  ...  Explicit causality, a subset of potential causality, tracks only relevant dependencies and reduces several of the potential dangers of causal consistency.  ...  ACKNOWLEDGMENTS The authors would like to thank Neil Conway, Aurojit Panda, and Patrick Wendell for constructive commentary on earlier versions of this manuscript.  ... 
doi:10.1145/2391229.2391251 dblp:conf/cloud/BailisFGHS12 fatcat:pwzeglmmmnde5pijnoajim7ici

Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning [article]

Sanghack Lee, Vasant Honavar
2015 arXiv   pre-print
Maier et al. (2010) introduced the relational causal model (RCM) for representing and inferring causal relationships in relational data.  ...  We revisit the definition of AGG and show that AGG, as defined in Maier et al. (2013b), does not correctly abstract all ground graphs.  ...  is co-sponsored by the Institute for Cyberscience, the Huck Institutes of the Life Sciences, and the Social Science Research Institute at the Pennsylvania State University.  ... 
arXiv:1508.02103v2 fatcat:jntrlz55kvgmnlccvfaiptv224

Learning Sparse Causal Models is not NP-hard [article]

Tom Claassen, Joris Mooij, Tom Heskes
2013 arXiv   pre-print
This paper shows that causal model discovery is not an NP-hard problem, in the sense that for sparse graphs bounded by node degree k the sound and complete causal model can be obtained in worst case order  ...  We present a modification of the well-known FCI algorithm that implements the method for an independence oracle, and suggest improvements for sample/real-world data versions.  ...  A causal DAG G C is a directed acyclic model where the arcs represent direct causal interactions (Pearl, 2000) .  ... 
arXiv:1309.6824v1 fatcat:li2f4txr6zaftenweqyb4igf5a

A Unifying Framework for Causal Explanation of Sequential Decision Making [article]

Samer B. Nashed and Saaduddin Mahmud and Claudia V. Goldman and Shlomo Zilberstein
2022 arXiv   pre-print
Building on the well-studied structural causal model paradigm for causal reasoning, we show how to identify semantically distinct types of explanations for agent actions using a single unified approach  ...  We present a novel framework for causal explanations of stochastic, sequential decision-making systems.  ...  Approximate Causal Models for MDPs There are many possible methods for building approximate causal graphs.  ... 
arXiv:2205.15462v1 fatcat:o4xaodfbvjg23grj7vdathciam

Stateless software model checking parameterized with memory consistency models

Levente Bajczi
2020 Zenodo  
Furthermore, I apply the algorithm to several well-known architectures and programs, and evaluate its performance compared to state-of-the-art software model checking [...]  ...  This algorithm builds on the stateless model checking approach, which yields a significantly lower memory usage than other techniques by using a smart exploration strategy to manage the large state space  ...  Model Checking Model checking is an area of formal verification, which uses a finite-state model of a system (in the case of software verification, a program) for checking whether it meets a given set  ... 
doi:10.5281/zenodo.5905769 fatcat:i2xdtgzyojay5bhdjmwngsplii

Causal sets from simple models of computation [article]

Tommaso Bolognesi
2010 arXiv   pre-print
For each model we identify the causality relation among computation events, implement it, and conduct a possibly exhaustive exploration of the associated causal set space, while examining quantitative  ...  Causality among events is widely recognized as a most fundamental structure of spacetime, and causal sets have been proposed as discrete models of the latter in the context of quantum gravity theories,  ...  We express our gratitude to Stephen Wolfram, Alex Lamb and Hans-Thomas Elze for various lively discussions on the topics covered in the paper, and to Wolfram Research ( for kindly granting  ... 
arXiv:1004.3128v1 fatcat:2nrrmuqesvbehl7c3zptzzinla

Relationship between Current Account Balance and Budget Balance: A Descriptive Analysis

Riaz Ahmed Dahar, Aisha Bashir Shah, Shahida Habib, Dr.Faiz-Muhammad Shaikh
2017 Zenodo  
We can see that the trend or behavior of both variables is different checked in graph 3 for most corrupt nations.  ...  But for more accurate and detailed results to check the behavior and relationship of both variables and therefore its also one of the major reasons that we are moving to the regression analysis  ...  We can see that the trend or behavior of both variables is different checked in graph 3 for most corrupt nations.  ... 
doi:10.5281/zenodo.3534727 fatcat:3x5n4ce7gfer7aziegsg7h3hqq

Graphical Models for Inference Under Outcome-Dependent Sampling

Vanessa Didelez, Svend Kreiner, Niels Keiding
2010 Statistical Science  
Moreover, we give sufficient graphical conditions for testing and estimating the causal effect of exposure on outcome. The practical use is illustrated with a number of examples.  ...  Graphical models represent assumptions about the conditional independencies among the variables.  ...  In addition to directed acyclic and undirected graphs, we want to point out that chain graphs provide a further class of useful models.  ... 
doi:10.1214/10-sts340 fatcat:crrcqsgdq5hmjplcpblonsvoti

Data Smashing 2.0: Sequence Likelihood (SL) Divergence For Fast Time Series Comparison [article]

Yi Huang, Ishanu Chattopadhyay
2019 arXiv   pre-print
Recognizing subtle historical patterns is central to modeling and forecasting problems in time series analysis.  ...  Here we introduce and develop a new approach to quantify deviations in the underlying hidden generators of observed data streams, resulting in a new efficiently computable universal metric for time series  ...  However, although a transient causal state could never be revisited, as the q in Example 1, it could also be revisited for infinitely many times.  ... 
arXiv:1909.12243v2 fatcat:aary3fjdorftllykm2ybpm5glm

Supporting the analytical reasoning process in information visualization

Yedendra Babu Shrinivasan, Jarke J. van Wijk
2008 Proceeding of the twenty-sixth annual CHI conference on Human factors in computing systems - CHI '08  
The analyst can revisit a visualization state from both the navigation and knowledge views to review the analysis and reuse it to look for alternate views.  ...  The navigation view provides an overview of the exploration process by capturing the visualization states automatically.  ...  We thank the reviewers and Hannes Pretorius for their insightful comments. We thank Jean-Bernard Martens for his guidance on conducting the user study.  ... 
doi:10.1145/1357054.1357247 dblp:conf/chi/ShrinivasanW08 fatcat:dquuivxszjeffb5k6anoyo6rry

A Characterization of Markov Equivalence Classes of Relational Causal Models under Path Semantics

Sanghack Lee, Vasant G. Honavar
2016 Conference on Uncertainty in Artificial Intelligence  
Relational Causal Models (RCM) generalize Causal Bayesian Networks so as to extend causal discovery to relational domains.  ...  Our analysis also suggests ways to improve the orientation recall of algorithms for learning the structure of RCM under bridge burning semantics as well.  ...  the Institute for Cyberscience, the Huck Institutes of the Life Sciences, and the Social Science Research Institute at the Pennsylvania State University.  ... 
dblp:conf/uai/LeeH16 fatcat:hgogyb6qsfea3jqn5zkpgzmxam
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