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Applications and tailor-made methods
Applications and tailor-made methods
Neuroscience
Y. Li, H. Li, ans S. K. Zhou. Causal Longitudinal Image Synthesis. arXiv:2410.17691, 2024.
[pdf] [Google scholar]J. Ji, Z. Zhang, L. Han, J. Liu. MetaCAE: Causal autoencoder with meta-knowledge transfer for brain effective connectivity estimation. Computers in Biology and Medicine, 170: 107940, 2024.
[pdf] [Google scholar]S. Chiyohara, J. Furukawa, T. Noda, J. Morimoto, and H. Imamizu. Proprioceptive short-term memory in passive motor learning. Scientific Reports, 13: 20826, 2023.
[pdf] [Google scholar]Z. Xia, T. Zhou, S. Mamoon, A. Alfakih, J. Lu A Structure-guided Effective and Temporal-lag Connectivity Network for Revealing Brain Disorder Mechanisms. Arxiv preprint arXiv:2212.00555, 2022.
[pdf] [Google scholar]V. Lam, R. Clarnette, R. Francis, M. Bynevelt, G. Watts, L. Flicker, C. F. Orr, P. Loh, N. Lautenschlager, C. M. Reid, J. K. Foster, S. S Dhaliwal, S. Robinson, E. Corti, M. Vaccarezza, B. Horgan, R. Takechi, and J. Mamo. Efficacy of probucol on cognitive function in Alzheimer’s disease: study protocol for a double-blind, placebo-controlled, randomised phase II trial (PIA study). BMJ Open, 12(2): e058826, 2022.
[pdf] [Google scholar]K. Bo, S. Yin, Y. Liu, Z. Hu, S. Meyyappan, S. Kim, A. Keil, M. Ding. Decoding Neural Representations of Affective Scenes in Retinotopic Visual Cortex. Cerebral Cortex, bhaa411, 2021.
[pdf] [Google scholar]G. Zhang, A. Zhang, B. Cai, Z. Tu, V. D. Calhoun, Y. P. Wang. Detecting abnormal connectivity in schizophrenia via a joint directed acyclic graph estimation model. Arxiv preprint arXiv:2010.13029, 2022.
[pdf] [Google scholar]T. Ogawa, H. Shimobayashi, J. Hirayama, M. Kawanabe. Asymmetric effective connectivity within frontoparietal motor network underlying motor imagery and motor execution. bioRxiv 2020.10.22.3511068.
[pdf] [Google scholar]R. Tu, K. Zhang, B. C. Bertilson, H. Kjellstöm, C, Zhang. Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation. In Advances in Neural Information Processing Systems 33 (NIPS2019), pp. xx-xx, 2019.
[pdf] [Google scholar]V. Fleischer, M. Muthuraman, A. R. Anwar, G. Gonzalez-Escamilla, A. Radetz, R.-M. Gracien, S. Bittner, F. Luessi, S. G. Meuth, F. Zipp, and S. Groppa . Continuous reorganization of cortical information flow in multiple sclerosis: A longitudinal fMRI effective connectivity study. Scientific Reports, 10: 806, 2020.
[pdf] [Google scholar]A. Zhang, G. Zhang, B. Cai, T. W. Wilson, J. M. Stephen, V. D. Calhoun, Y.-P. Wang. A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence. Arxiv preprint arXiv:2006.12618, 2020.
[pdf] [Google schlar]A. Zhang, G. Zhang, B. Cai, W. Hu, L. Xiao, T. W. Wilson, J. M. Stephen, V. D. Calhoun, Y.-P. Wang. Causal inference of brain connectivity from fMRI with ψ-Learning Incorporated Linear non-Gaussian Acyclic Model (ψ-LiNGAM). Arxiv preprint arXiv:2006.09536, 2020.
[pdf] [Google schlar]B. Huang, K. Zhang, R. Sanchez-Romero, J. Ramsey, M. Glymour, C. Glymour. Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data. Arxiv preprint arXiv:1902.10073, 2019.
[pdf] [Google scholar]J. Ji, J. Liu, A. Zou, A. Zhang. ACOEC-FD: Ant Colony Optimization for Learning Brain Effective Connectivity Networks From Functional MRI and Diffusion Tensor Imaging. Frontiers in Neuroscience, 13: 1290, 2019.
[pdf] [Google scholar]R. Sanchez-Romero, J. D. Ramsey, K. Zhang, M. R. K. Glymour, B. Huang, and C. Glymour. Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods. Network Neuroscience, pp. xx--xx, 2018.
[pdf] [Google scholar]R. Sanchez-Romero, J. D. Ramsey, K. Zhang, M. R. K. Glymour, B. Huang, and C. Glymour. Causal discovery of feedback networks with functional magnetic resonance imaging. bioRxiv 245936, 2018.
[pdf] [Google scholar]C. Mills-Finnerty, C. Hanson, S. J. Hanson. Brain network connectivity underlying decisions between the" lesser of two evils". PeerJ Preprints 5:e3340v1, 2017.
[pdf] [Google scholar]N. Bielczyk, A. Llera, J. Buitelaar, J. Glennon, and C. Beckmann. Momentum: a new approach to causality in functional Magnetic Resonance Imaging. Arxiv preprint arXiv:1606.08724, 2016.
[pdf] [Google scholar]A. Manelis, J. R. C. Almeida, R. Stiffler, J. C. Lockovich, H. A. Aslam, and M. L. Phillips. Anticipation-related brain connectivity in bipolar and unipolar depression: a graph theory approach. Brain, pp. xx--xx, 2016.
[pdf] [Google scholar]I. Hettiarachchi, S. Mohamed, S. Nahavandi, and S. Nahavandi. Application of extended multivariate modeling for information flow analysis of event related responses. In Proc. 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC2015), pp. 1845-1851, Kowloon Tong, Hong Kong, 2015.
[pdf] [Google scholar]Y. Liu, X. Wu, J. Zhang, X. Guo, Z. Long, and L. Yao. Altered effective connectivity model in the default mode network between bipolar and unipolar depression based on resting-state fMRI. Journal of Affective Disorders, xx: xx--xx, 2015.
[pdf] [Google scholar]J. M. Spielberg, R. E. McGlinchey and W. P. Milberg and D. H. Salat. Brain network disturbance related to posttraumatic stress & traumatic brain injury in veterans. Biological Psychiatry, xx: xx--xx, 2015.
[pdf] [Google scholar]E. Dobryakova, O. Boukrina and G. R Wylie. Investigation of information flow during a novel working memory task in individuals with traumatic brain injury. Brain Connectivity, xx: xx--xx, 2015.
[pdf] [Google scholar]C. Mills-Finnerty, C. Hanson and S. J. Hanson. Brain network response underlying decisions about abstract reinforcers. NeuroImage, 103: 48--54, 2014.
[pdf] [Google scholar]L. Xu, T. Fan, X. Wu, K. Chen, X. Guo, J. Zhang and L. Yao. A pooling-LiNGAM algorithm for effective connectivity analysis of fMRI data. Frontiers in Computational Neuroscience, 8:125, 2014.
[pdf] [Google scholar]L. Schiatti, G. Nollo, G. Rossato and L. Faes. Investigating cardiovascular and cerebrovascular variability in postural syncope by means of extended Granger causality. In Proc. 8th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO2014), pp.43-44, Trento, Italy, 2014.
[pdf] [Google scholar]A. Manelis and L. M. Reder. Effective connectivity among the working memory regions during preparation for and during performance of the n-back task. Frontiers in Human Neuroscience, 8:593, 2014.
[pdf] [Google scholar]J. D. Ramsey, R. Sanchez-Romero and C. Glymour. Non-Gaussian methods and high-pass filters in the estimation of effective connections. NeuroImage, 84(1): 986--1006, 2014.
[pdf] [TETRAD IV] [Google scholar]O. Boukrina and W. W. Graves. Neural networks underlying contributions from semantics in reading aloud. Frontiers in Human Neuroscience, 7:518, 2013.
[pdf] [Google scholar]D. A. Dawson, K. Cha, L. B. Lewis, J. D. Mendola, A. Shmuel. Evaluation and calibration of functional network modeling methods based on known anatomical connections. NeuroImage, 67: 331-343, 2013.
[pdf] [Google scholar]J. D. Ramsey, S. J. Hanson and C. Glymour. Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study. NeuroImage, 58(3): 838--848, 2011.
[pdf] [TETRAD IV] [Google scholar]S. M. Smith, K. L. Miller, G. Salimi-Khorshidi, M. Webster, C. F. Beckmann, T. E. Nichols, J. D. Ramsey and M. W. Woolrich. Network modelling methods for FMRI. NeuroImage, 54(2): 875--891, 2011.
[pdf] [simulated fMRI data] [Google scholar]
Economics/Finance/Marketing
NEW D. C. Oliveira, Y. Lu, X. Lin, M. Cucuringu, A. Fujita. Causality-Inspired Models for Financial Time Series Forecasting. arXiv:2408.09960, 2024.
[pdf] [Google scholar]R. Tang. Trading with Time Series Causal Discovery: An Empirical Study. arXiv:2408.15846, 2024.
[pdf] [Google scholar]D. C. Oliveira, Y. Lu, X. Lin, M. Cucuringu, A. Fujita. Causality-Inspired Models for Financial Time Series Forecasting. arXiv:2405.01078, 2024.
[pdf] [Google scholar]B. Jin, X. Xu. Contemporaneous causality among price indices of ten major steel products. Ironmaking & Steelmaking: Processes, Products and Applications, xx(xx): xx-xx, 2024.
[pdf] [Google scholar]Y. Jiang and S. Shimizu. Does Financial Literacy Impact Investment Participation and Retirement Planning in Japan?. arXiv:2405.01078, 2024.
[pdf] [Google scholar]F. Montoya and H. Astudillo. Causal Graph: Interpretation of Causal Relationships in Temporary Deviations of Business Processes. In Proc. 2023 XLIX Latin American Computer Conference (CLEI), 2023.
[pdf] [Google scholar]R. Taguchi, H. Sakaji, K. Izumi, Y. Murayama. Asset Allocation Method Based on Sentiment Signals and Causal Information using Multi-asset Classes. International Journal of Smart Computing and Artificial Intelligence, 7(2): IJSCAI827, 2023.
[pdf] [Google scholar]X. Xu, Y. Zhang. Contemporaneous causality among office property prices of major Chinese cities with vector error correction modeling and directed acyclic graphs. Journal of Modelling in Management, xx(xx): xx–xx, 2023.
[pdf] [Google scholar]Y. Jiang and S. Shimizu. Linkages among the Foreign Exchange, Stock, and Bond Markets in Japan and the United States. In Proc. Causal Analysis Workshop 2023 (CAWS2023), PMLR xx:xx-xx, 2023.
[pdf] [Google scholar]K. Duangnate, J. W. Mjelde. Changing Regional Price Relationships in Retail Fresh Broiler/Fryer Whole Chicken Prices. Journal of Agricultural and Applied Economics, xx(xx): xx–xx, 2023.
[pdf] [Google scholar]F. Cordoni, N. Dorémus, A. Moneta. Identification of Vector Autoregressive Models with Nonlinear Contemporaneous Structure. LEM WORKING PAPER SERIES, 2023.
[pdf] [Google scholar]M. de Mier, F. Delbianco, F. Tohmé, L. Patrizio, F. Rodriguez, M. R. Stéfani. Causality by Vote: Aggregating Evidence on Causal Relations in Economic Growth Processes. Working Papers 260, Red Nacional de Investigadores en Economía (RedNIE), 2023.
[pdf] [Google scholar]X. Xu, Y. Zhang. An integrated vector error correction and directed acyclic graph method for investigating contemporaneous causalities. International Mineral Economics, xx(xx): xx–xx, 2023.
[pdf] [Google scholar]X. Xu, Y. Zhang. An integrated vector error correction and directed acyclic graph method for investigating contemporaneous causalities. International Journal of Real Estate Studies, 17(1): 148–157, 2023.
[pdf] [Google scholar]T. Ciarli, A. Coad, A. Moneta. Does exporting cause productivity growth? Evidence from Chilean firms. Structural Change and Economic Dynamics, xx(xx): xx-xx, 2023.
[pdf] [Google scholar]X. Xu, Y. Zhang. An integrated vector error correction and directed acyclic graph method for investigating contemporaneous causalities. Decision Analytics Journal, 7(xx): xx-xx, 2023.
[pdf] [Google scholar]S. Lee and S. Lee. Statistical process monitoring for vector autoregressive time series based on location-scale CUSUM method. Journal of Statistical Computation and Simulation, xx(xx): xx-xx, 2022.
[pdf] [Google scholar]T. Yoshihara, T. Kaizoji. The Evolving Causal Structure of Equity Risk Factors. Arxiv preprint arXiv:2211.16176, 2022.
[pdf] [Google scholar]B. D. Deaton. Foreign exchange market linkages, 2017-2019. International Journal of Accounting, Economics & Finance Perspectives. 2(1): 68-83, 2022.
[pdf] [Google scholar]X. Xu & Y. Zhang. Contemporaneous causality among residential housing prices of ten major Chinese cities. International Journal of Housing Markets and Analysis, xx(x): xx-xx, 2022.
[pdf] [Google scholar]X. Xu & Y. Zhang. Contemporaneous causality among one hundred Chinese cities. Empirical Economics, xx(x): xx-xx, 2022.
[pdf] [Google scholar]G. D'Acunto, P. Bajardi, F. Bonchi, G. D. F. Morales. The Evolving Causal Structure of Equity Risk Factors. Arxiv preprint arXiv:2111.05072, 2021.
[pdf] [Google scholar]T. Moriyama, M. Kuwano. Causal inference for contemporaneous effects and its application to tourism product sales data. Journal of Marketing Analytics, xx(x): xx-xx, 2021.
[pdf] [Google scholar]S. B. Bruns, A. Moneta, D. Stern. Estimating the economy-wide rebound effect using empirically identified structural vector autoregressions. Energy Economics, xx(x): xx-xx, 2021.
[pdf] [Google scholar]H. Ohmura. The connection between stock market prices and political support: evidence from Japan. Applied Economics Letters, xx(x): xx-xx, 2020.
[pdf] [Google scholar]J. Luo and Q. Zhang. Risk contagions between global oil markets and China’s agricultural commodity markets under structural breaks. Applied Economics, xx(x): xx-xx, 2020.
[pdf] [Google scholar]B. D. Deaton. The JPY/AUD Carry Trade and Its Causal Linkages to Other Markets. Journal of Applied Business and Economics xx(x): xx-xx, 2020.
[pdf] [Google scholar]E. Brancaccio, A. Moneta, M. Lopreite, A. Califano. Nonperforming Loans and Competing Rules of Monetary Policy: a Statistical Identification Approach. Structural Change and Economic Dynamics, xx(x): xx-xx, 2020.
[pdf] [Google scholar]S. B. Bruns, A. Moneta, D. I. Stern. Estimating the Economy-Wide Rebound Effect Using Empirically Identified Structural Vector Autoregressions. LEM WORKING PAPER SERIES: 2019/27, 2019.
[pdf] [Google scholar]T. Ciarli, A. Coad, A. Moneta. Exporting and productivity as part of the growth process: Causal evidence from a data-driven structural VAR. LEM WORKING PAPER SERIES: 2019/39, 2019.
[pdf] [Google scholar]G. Ecchia, F. Gagliardi, C. Giannetti. SOCIAL INVESTMENT AND YOUTH LABOR MARKET PARTICIPATION. Contemporary Economic Policy, xx(x): xx-xx, 2019.
[pdf] [Google scholar]X. Xu. Contemporaneous Causal Orderings of CSI300 and Futures Prices through Directed Acyclic Graphs. Economics Bulletin, 39(3): 2052-2077, 2019.
[pdf] [Google scholar]K. H. Al-yahyaee, A. K. Tiwari, I. M. W. Al-Jarrah, W. Mensi. Testing for the Granger-causality between returns in the U.S. and GIPSI stock markets. Physica A: Statistical Mechanics and its Applications, xx(xx): xx-xx, 2019.
[pdf] [Google scholar]X. Xu. Contemporaneous and Granger causality among US corn cash and futures prices. European Review of Agricultural Economics, xx(xx): xx-xx, 2018.
[pdf] [Google scholar]G. Castañeda, F. Chávez-Juárez, O. A.Guerrero. How do governments determine policy priorities? Studying development strategies through spillover networks. Journal of Economic Behavior & Organization, xx(xx): xx, 2018.
[pdf] [Google scholar]O. Guerrero and G. Castañeda. The Resilience of Public Policies in Economic Development. Complexity, xx(xx): xx, 2018.
[pdf] [Google scholar]B. D. Deaton. Effects of the Swiss Franc/Euro exchange rate floor on the calibration of probability forecasts. Forecasting, 1(1): 2, 2018.
[pdf] [Google scholar]J. Chen, S. Kibriya, D. Bessler, and E. Price. The relationship between conflict events and commodity prices in Sudan. Journal of Policy Modeling, xx(xx): xx-xx, 2018.
[pdf] [Google scholar]R. Guo, L. Cheng, J. Li, P. R. Hahn, H. Liu. A Survey of Learning Causality with Data: Problems and Methods. Arxiv preprint arXiv:1809.09337, 2018.
[pdf] [Google scholar]H. Herwartz. Hodges–Lehmann detection of structural shocks – An analysis of macroeconomic dynamics in the Euro area. Oxford Bulletin of Economics and Statistics, xx(xx): xx-xx, 2018.
[pdf] [Google scholar]M. Guerini, A. Moneta, M. Napoletano, and A. Roventini. The Janus-faced nature of debt: results from a data-driven cointegrated SVAR approach. xx, 2017.
[pdf] [Google scholar]G. Fagiolo, M. Guerini, F. Lamperti, A. Moneta, and A. Roventini. Validation of agent-based models in economics and finance. LEM WORKING PAPER SERIES: 2017/23, 2017.
[pdf] [Google scholar]M-K. Kim, H. Tejeda, and T. E. Yu. U.S. milled rice markets and integration across regions and types. International Food and Agribusiness Management Review, xx(xx): xx-xx, 2017.
[pdf] [Google scholar]A. Coad, M. Cowling, and J. Siepel. Growth processes of high-growth firms as a four-dimensional chicken and egg. Industrial and Corporate Change, dtw040, 2017.
[pdf] [Google scholar]W. Huang, P.-C. Lai, and D. A. Bessler. On the changing structure among Chinese equity markets: Hong Kong, Shanghai, and Shenzhen. European Journal of Operational Research, xx(xx): xx-xx, 2017.
[pdf] [Google scholar]P. Puonti. Fiscal multipliers in a structural VEC model with mixed normal errors. Journal of Macroeconomics, 48: 144-154, 2016.
[pdf] [Google scholar]X. Xu. Contemporaneous causal orderings of US corn cash prices through directed acyclic graphs. Empirical Economics, xx(xx): xx-xx, 2016.
[pdf] [Google scholar]S. Zhao, Y. Tong, X. Liu, and S. Tan. Correlating Twitter with the stock market through non-Gaussian SVAR. In Proc. 8th International Conference on Advanced Computational Intelligence (ICACI2016), pp. 257-264, Chiang Mai, Thailand, 2016.
[pdf] [Google scholar]T. Brenner and M. Duscshl. Causal dynamic effects in regional systems of technological activities: a SVAR approach. The Annals of Regional Science, xx(xx): xx-xx, 2015.
[pdf] [Google scholar]P.-C. Lai and D. A. Bessler. Price discovery between carbonated soft drink manufacturers and retailers: A disaggregate analysis with PC and LiNGAM algorithms. Journal of Applied Economics, 18(1): 173-197, 2015.
[pdf] [Google scholar]J.-C. Bizimana, J. P. Angerer, D. A. Bessler and F. Keita. Cattle markets integration and price discovery: The case of Mali. Journal of Development Studies, 51(3): 319-334, 2015.
[pdf] [Google scholar]A. Coad and M. Binder. Causal linkages between work and life satisfaction and their determinants in a structural VAR approach. Economics Letters, 124(2): 263-268, 2014.
[pdf] [Google scholar]A. Coad, M. Cowling, and J. Siepel. Growth processes of high-growth firms in the UK. NESTA working paper 12/10, 2012.
[pdf] [Google scholar]Z. Gao, Z. Wang, L. Wang. and S. Tan. Linear non-Gaussian causal discovery from a composite set of major US macroeconomic factors. Expert Systems with Applications, 39(12): 10867--10872, 2012.
[pdf] [Google scholar]E. Ferkingsta, A. Lølanda and M. Wilhelmsen. Causal modeling and inference for electricity markets. Energy Economics, 33(3): 404--412, 2011.
[pdf] [Google scholar]A. Moneta, D. Entner, P. O. Hoyer and A. Coad. Causal inference by independent component analysis: Theory and applications. Oxford Bulletin of Economics and Statistics, 75(5): 705-730, 2013.
[pdf] [code] [Google scholar]Z. Wang and S. Tan. Automatic linear causal relationship identification for financial factor modeling. Expert Systems with Applications, 36(10): 12441--12445, 2009.
[pdf] [Google scholar]
Epidemiology
R. Mojtabai. Problematic Social Media Use and Internalizing Symptoms in Adolescents. Journal of Policy Modeling, xx(xx): xx-xx, 2023.
[pdf] [Google scholar]E. L. Barrera and D. Miljkovic. The link between the two epidemics provides an opportunity to remedy obesity while dealing with Covid-19. Journal of Policy Modeling, xx(xx): xx-xx, 2022.
[pdf] [Google scholar]R García-Velázquez, M. Jokela, T. H. Rosenström. Direction of Dependence Between Specific Symptoms of Depression: A Non-Gaussian Approach. Clinical Psychological Science, xx(xx): xx-xx, 2019.
[pdf] [Google scholar]H. Helajärvi, T. Rosenström, K. Pahkala, M. Kähönen, T. Lehtimäki, O. J. Heinonen, M. Oikonen, T. Tammelin, J. S. A. Viikari and O. T. Raitakari. Exploring causality between TV viewing and weight change in young and middle-aged adults. The cardiovascular risk in young Finns study. PLoS ONE, 9(7): e101860, 2014.
[pdf] [Google scholar]T. Rosenström, M. Jokela, S. Puttonen, M. Hintsanen, L. Pulkki-Råback, J. S. Viikari, O. T. Raitakari and L. Keltikangas-Järvinen. Pairwise measures of causal direction in the epidemiology of sleep problems and depression. PLoS ONE, 7(11): e50841, 2012.
[pdf] [WLS data] [Google scholar]
Psychology
R Mojtabai. Problematic social media use and psychological symptoms in adolescents. Social Psychiatry and Psychiatric Epidemiology, xx(xx): xx-xx, 2024.
[pdf] [Google scholar]T. H. Rosenström, N. O. Czajkowski, O. A. Solbakken, S. E. Saarni. Direction of dependence analysis for pre-post assessments using non-Gaussian methods: a tutorial. Psychotherapy Research, 2023.
[pdf] [Google scholar]T. Itahashi, N. Okada, S. Ando, S. Yamasaki, D. Koshiyama, K. Morita, N. Yahata, S. Koike, A. Nishida, K. Kasai, R. Hashimoto. Functional connectome linking child-parent relationships with psychological problems in adolescence. bioRxiv 678714, 2019.
[pdf] [Google scholar]A. von Eye and R. P. DeShon. Decisions concerning directional dependence. International Journal of Behavioral Development, 36(4): 323-326, 2012.
[pdf] [Google scholar]S. Pornprasertmanit and T. D. Little. Determining directional dependency in causal associations. International Journal of Behavioral Development, 36(4): 313-322, 2012.
[pdf] [Google scholar]A. von Eye and R. P. DeShon. Directional dependence in developmental research. International Journal of Behavioral Development, 36(4): 303-312, 2012.
[pdf] [Google scholar]Y. Takahashi, K. Ozaki, B. W. Robert and J. Ando. Can low behavioral activation system predict depressive mood?: An application of non-normal structural equation modeling. Japanese Psychological Research, 54(2): 170-181, 2012.
[pdf] [Google scholar]
Chemistry
A. Ghosh and S. Ghosh. Mapping causal pathways with structural modes fingerprint for perovskite oxides. Machine Learning: Science and Technology, 5: 045014, 2024.
[pdf] [Google scholar]H. Dewantoro, A. Smith, P. Daoutidis. Causal Discovery for Topology Reconstruction in Industrial Chemical Processes. Industrial & Engineering Chemistry Research, 63(26): xx-xx, 2024.
[pdf] [Google scholar]J. Luo, Z. Jin, H. Jin, Q. Li, X. Ji, Y. Dai. Causal temporal graph attention network for fault diagnosis of chemical processes. Chinese Journal of Chemical Engineering, xx(xx): xx-xx, 2024.
[pdf] [Google scholar]P. Campomanes, M. Neri, B. A.C. Horta, U. F. Roehrig, S. Vanni, I. Tavernelli and U. Rothlisberger. Origin of the spectral shifts among the early intermediates of the rhodopsin photocycle. Journal of the American Chemical Society, 136(10): 3842-3851, 2014.
[pdf] [Google scholar]
Others
NEW O. Faruque, X. Li, M. A. Khan, H. Alam, J Wang. Comparative Evaluation of Causal Discovery and Inference Approaches on Arctic Sea Ice Time Series Data. In IEEE International Conference on Big Data, pages. 4588-4597, 2024
[pdf] [Google scholar]NEW B. Youngmann, M. Cafarella, A. Gilad, S. Roy. Summarized Causal Explanations For Aggregate Views. In Proc. ACM on Management of Data, 2 (1): Article No.: 71, 2025
[pdf] [Google scholar]NEW S. Mohseni-Sehdeh, W. Saad, K. Sakaguchi, T. Yu. Causal Digital Twins for Physical System Control. In Proc. 59th Annual Conference on Information Sciences and Systems (CISS), ), pp. 393 - 398, 2025
[pdf] [Google scholar]NEW Q. Li, T. Qian, H. Wang, L. Bai, R. Long. Environmental forcing and policy synergy: A multidimensional approach in the governance of air pollution and carbon emission. Environmental Research, 261(15): 119747, 2025.
[pdf] [Google scholar]NEW Q. Zhang, Z. Ma, Y. Wu, Y. Liu, X. Qu. Quantifying variable contributions to bus operation delays considering causal relationships. Transportation Research Part E: Logistics and Transportation Review, 194: 103881, 2025.
[pdf] [Google scholar]NEW Y. Tan, Y. Zeng, Q. Wang. Analysis and prediction of sea level rise along the U.S. East and Gulf coasts and its socio-economic impacts on the nearby inland areas. Theoretical and Applied Climatology, 156(2): Article Number 2, 2025.
[pdf] [Google scholar]NEW S. Majumder, ANM. N Abeer, M. Rahman, M. A. E. Bhuiyan. Accurate estimation of global horizontal irradiance from Himawari-8 satellite data using interpretable hybrid models that reduce shortcut learning. Evolving Earth, 3(1): 100051, 2025.
[pdf] [Google scholar]F. Montoya, C. Beltran, H. Astudillo. Causal Organizational Mining in Software Engineering: Evaluating Improvement Strategies in Development Team Dynamics. In Proc. 2024 L Latin American Computer Conference (CLEI), 2024.
[pdf] [Google scholar]V. C. Palanki. Leveraging Causal Inference Techniques for Robust Root Cause Identification in Complex Systems. Journal of Artificial Intelligence, Machine Learning and Data Science, 2(3): xx-xx, 2024.
[pdf] [Google scholar]Y. Nishi, K. Ikuno, Y. Takamura, Y. Minamikawa, S. Morioka. Modeling the Heterogeneity of Post-Stroke Gait Control in Free-Living Environments Using a Personalized Causal Network. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 32: xx-xx, 2024.
[pdf] [Google scholar]J. Xue, T. Zhang, H. Ye. KPI-oriented process monitoring based on causal-weighted partial least squares. Information Sciences, 689: 121470, 2024.
[pdf] [Google scholar]L. Pham, H. Ha, H. Zhang . Root Cause Analysis for Microservices based on Causal Inference: How Far Are We?. arXiv:2408.13729, 2024.
[pdf] [Google scholar]Z. An, C. Mullen, X. Guan, D. Ettema, E. Heinen. Shared micromobility, perceived accessibility, and social capital. Transportation, xx: xx-xx, 2024.
[pdf] [Google scholar]Y. Taguchi, A. Kurotani, H. Yamano, H. Miyamoto, T. Kato, N. Tsuji, M. Matsuura, T. Nakaguma, T. Etoh, Y. Shiotsuka, R. Fujino, M. Udagawa, J. Kikuchi, H. Ohno, H. Takahashi. Causal estimation of maternal-offspring gut commensal bacterial associations under livestock grazing management conditions. Computational and Structural Biotechnology Reports, 100012, 2024.
[pdf] [Google scholar]H.Ş. Bozcuk, M.S. Alemdar. Solving the puzzle of quality of life in cancer: integrating causal inference and machine learning for data-driven insights. Health Qual Life Outcomes, 22: 60, 2024.
[pdf] [Google scholar]J. J. Nichol, M. Weylandt, M. Fricke, M. E. Moses, D. L. Bull, L. P. Swiler. Space-Time Causal Discovery in Climate Science: A Local Stencil Learning Approach. ESS Open Archive. August 01, 2024.
[pdf] [Google scholar]C. Symvoulidis, E. Paraskevoulakou, A. Kiourtis, A. Mavrogiorgou, D. Kyriazis. Dynamic Resource Allocation on the Edge: A Causal and Contextually-Aware Machine Learning Approach. In Proc. Intelligent Systems and Applications (IntelliSys 2024), 2024.
[pdf] [Google scholar]T. Zhang, D. Rinaldi, F. Pianese, A. Aghasaryan. Evaluating the Robustness of Causal Discovery Algorithms with Observations and Interventions in VNF Deployments. 9th Causal Inference Workshop at UAI 2024, 2024.
[pdf] [Google scholar]S. R. O. Peralta, H. Washizaki, Y. Fukazawa, Y. Noyori, S. Nojiri, H. Kanuka. Unraveling the Influences on Bug Fixing Time: A Comparative Analysis of Causal Inference Model. In Proc. 28th International Conference on Evaluation and Assessment in Software Engineering (EASE '24), pp. 393 - 398, 2024
[pdf] [Google scholar]M. Maisonnave, F. Delbianco, F. Tohmé, E. Milios, A. Maguitman. Learning causality structures from electricity demand data. Energy Systems, xx: xx-xx, 2024
[pdf] [Google scholar]H. Zhao, P. Xu, T. Gao, J. J. Zhang, J. Xu, D. W. Gao. CPTCFS: CausalPatchTST incorporated causal feature selection model for short-term wind power forecasting of newly built wind farms. International Journal of Electrical Power & Energy Systems, 160: 110059, 2024
[pdf] [Google scholar]Y. Chen, W. Wei, L. Wang, Y. Dong, C. J. Liang. Where do they go next? Causal inference-based prediction and visual analysis of graduates’ first destination. Journal of Visualization, xx(xx): xx-xx, 2024
[pdf] [Google scholar]Y. Chen, W. Wei, L. Wang, Y. Dong, C. J. Liang. Where do they go next? Causal inference-based prediction and visual analysis of graduates’ first destination. Journal of Visualization, xx(xx): xx-xx, 2024
[pdf] [Google scholar]C. D. Slaten, W. Wiedermann, M. S. Williams, B. Sebastian. Evaluating the Causal Structure of the Relationship Between Belonging and Academic Self-Efficacy in Community College: An Application of Direction Dependence Analysis. Innovative Higher Education, xx(xx): xx-xx, 2024
[pdf] [Google scholar]Z. Fan, W. Li, K. Laskey, K. C. Chang. Towards Personalized Anti-Phishing: Counterfactual Explanation Approach. Papers in Evolutionary Economic Geography, PREPRINT (Version 1) available at Research Square, 2024
[pdf] [Google scholar]C. Nast, T. Broekel, D. Entner. Fueling the Fire? How Government Support Drives Technological Progress and Complexity. Papers in Evolutionary Economic Geography, # 24.07, 2024.
[pdf] [Google scholar]Z. R. Fox, A. Ghosh. Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions. arXiv preprint arXiv:2404.04224, 2024.
[pdf] [Google scholar]Z. Wang, P. Krishnakumari, K. Anupam, H. van Lint, S. Erkens. A causal discovery approach to study key mixed traffic‐related factors and age of highway affecting raveling. Computer-Aided Civil and Infrastructure Engineering, xx(xx):xx-xx, 2024.
[pdf] [Google scholar]Q. Zhang, Z. Ma, Y. Ling, Z. Qin, P. Zhang, Z. Zhao. Causal Graph Discovery for Urban Bus Operation Delays: A case in Stockholm. The 103rd Transportation Research Board (TRB) Annual Meeting, January 7–11, 2024, Washington, DC, USA, 2024.
[pdf] [Google scholar]Y. Tamura, Y. Takai, H. Miyamoto, L. SeokHyun, Y. Liu, X. Qiu d, l. J. Kang, Y. Simasaki, C. Shindo, W. Suda, H. Ohno, Y. Oshima. Alteration of shoaling behavior and dysbiosis in the gut of medaka (Oryzias latipes) exposed to 2-μm polystyrene microplastics. Chemosphere, 353: 141643, 2024.
[pdf] [Google scholar]L. Giamattei, A. Guerriero, I. Malavolta, C. Mascia, R. Pietrantuono, S. Russo. Identifying Performance Issues in Microservice Architectures through Causal Reasoning. 5th ACM/IEEE International Conference on Automation of Software Test (AST 2024), 2024.
[pdf] [Google scholar]T. Nagase, A. O. N. Rene, K. Okuhara. Causal Discovery from Psychological States to Walking Behaviors for Pedestrians Interacting an APMV Equipped with eHMIs. ICIC Express Letters, Part B: Applications, 14(5): 475-482, 2024.
[pdf] [Google scholar]H. Liu, Y. Li, T. Hiraoka, T. Wada. Causal Discovery from Psychological States to Walking Behaviors for Pedestrians Interacting an APMV Equipped with eHMIs. xx, 2024.
[pdf] [Google scholar]B. Youngmann , M. Cafarella, A. Gilad, S. Roy. Summarized Causal Explanations For Aggregate Views. Proceedings of the ACM on Management of Data, 21(1): Article No.: 71, 2024.
[pdf] [Google scholar]谷岡 広樹, 佐藤 充宏, 柘植 竜治. サッカースクールにおける楽しさに関するアンケート分析と因果探索. 情報処理学会論文誌デジタルプラクティス, 4(3): 74-87, 2024.
[pdf] [Google scholar]M. Iatrou, M. Tziouvalekas, A. Tsitouras, E. Evangelou, C. Noulas, D. Vlachostergios, V. Aschonitis, G. Arampatzis, I. Metaxa, C. Karydas, and P. Tziachris. Analyzing the Impact of Storm 'Daniel'and Subsequent Flooding on Thessaly's Soil Chemistry through Causal Inference. Agriculture, 14, 549, 2024.
[pdf] [Google scholar]K. Ito, H. Miyamoto, M. Matsuura, C. Ishii, Y. Nakanishi, W. Suda, T. Satoh, F. Honda, A. Kurotani, N. Tsuji, T. Nakaguma, C. Shindo, T. Ito, R. Michibata, R. Yamada, Y. Takahashi, H. Kodama, J. Kikuchi, H. Ohno. A thermoprotective probiotic function by thermostable lactic acid bacteria and its causal structure. Journal of Functional Foods, 113: 106001, 2024.
[pdf] [Google scholar]M. Q. Siow, Y. S. Kim, M. J. Noh, M. M. C. Han. Exploring the Impact of Pesticide Usage on Crop Condition: A Causal Analysis of Agricultural Factors. Smart Media Journal, 12(10): ISSN:2287-1322, 2023.
[pdf] [Google scholar]K. Feng, H. Jiang, C. Yin, H. Sun. Gene regulatory network inference based on causal discovery integrating with graph neural network. Quantitative Biology, 11(4): 434-450, 2023.
[pdf] [Google scholar]A. Kurotani, H. Miyamoto, J. Kikuchi. Validation of causal inference data using DirectLiNGAM in an environmental small-scale model and calculation settings. MethodsX, 102528, 2024.
[pdf] [Google scholar]F. Fournier, L. Limonad, I. Skarbovsky, Y. David. The WHY in Business Processes: Discovery of Causal Execution Dependencies. arXiv:2310.14975, 2023.
[pdf] [Google scholar]A. Koshida, S. Karashima, K. Ogura, Y. Miyajima, K. Ogai, R. Mizoguchi, Y. Ikagawa, S. Hara, I. Mizushima, H. Fujii, M. Kawano, H. Tsujiguchi, A. Hara, H. Nakamura, and S. Okamoto. Impact of gut microbiome on serum IgG4 levels in the general population: Shika-machi super preventive health examination results. Frontiers in Cellular and Infection Microbiology, 13:1272398, 2023.
[pdf] [Google scholar]D. Ribeiro, F. Ferraz, M. B. Lopes, S. Rodrigues, P. Reis Costa, S. Vinga, A. M. Carvalho. Causal Graph Discovery for Explainable Insights on Marine Biotoxin Shellfish Contamination. In Proc. Intelligent Data Engineering and Automated Learning (IDEAL 2023), pp. 483–494, 2023.
[pdf] [Google scholar]D. Fahland, F. Fournier, L. Limonad, I. Skarbovsky and A. J.E. Swevels. Why are my Pizzas late? PMAI’23:ProcessManagementintheAIera, Aug19, 2023, Macao, S.A.R.
[pdf] [Google scholar]S. Al-Ali, J. Llopis-Lorente, M. T. Mora, M. Sermesant, B. Trénor, I. Balelli. A causal discovery approach for streamline ion channels selection to improve drug-induced TdP risk assessment. hal-04105144, 2023.
[pdf] [Google scholar]W. Liu, F. Liu, W. Fang, P. E. D. Love. Causal discovery and reasoning for geotechnical risk analysis. Reliability Engineering & System Safety, xx(xx): xx-xx, 2024.
[pdf] [Google scholar]J. Singh, K. Bharti, Arvind. Inferring physical laws by artificial intelligence based causal models. arXiv:2308.15395, 2023.
[pdf] [Google scholar]R. Taguchi, H. Sakaji, K. Izumi, Y. Murayama. Constructing Sentiment Signal-Based Asset Allocation Method with Causality Information. New Generation Computing, xx(xx): xx-xx, 2023.
[pdf] [Google scholar]M. Chevalley, J. Sackett-Sanders, Y. Roohani, P. Notin, A. Bakulin, D. Brzezinski, K. Deng, Y. Guan, J. Hong, M. Ibrahim, W. Kotlowski, M. Kowiel, P. Misiakos, A. Nazaret, M. Püschel, C. Wendler, A. Mehrjou, P. Schwab. The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data. arXiv:2308.15395, 2023.
[pdf] [Google scholar]A. Aït-Bachir, C. K. Assaad, C. de Bignicourt, E. Devijver, S. Ferreira, E. Gaussier, H. Mohanna, L. Zan. Case Studies of Causal Discovery from IT Monitoring Time Series. arXiv:2307.15678, 2023.
[pdf] [Google scholar]S. Al-Ali, J. Llopis-Lorente, M. T. Mora, M. Sermesant, B. Trénor, I. Balelli. A causal discovery approach for streamline ion channels selection to improve drug-induced TdP risk assessment. hal-04105144, 2023.
[pdf] [Google scholar]T. Saito, K. Fujiwara. Causal analysis of nitrogen oxides emissions process in coal-fired power plant with LiNGAM. Frontiers in Analytical Science, 2023.
[pdf] [Google scholar]R. Mizoguchi, S. Karashima, Y. Miyajima, K. Ogura, M. Kometani, D. Aono, S. Konishi, M. Demura, H. Tsujiguchi, A. Hara, H. Nakamura, T. Yoneda, S. Okamoto, and K. Satou . Impact of gut microbiome on the renin-aldosterone system: Shika-machi Super Preventive Health Examination results. Hypertension Research, 2023.
[pdf] [Google scholar]S. Okada, Y. Inabu, H. Miyamoto, K. Suzuki, T. Kato, A. Kurotani, Y. Taguchi, R. Fujino, Y. Shiotsuka, T. Etoh, N. Tsuji, M. Matsuura, A. Tsuboi, A. Saito, H. Masuya, J. Kikuchi, Y. Nagasawa, A. Hirose, T. Hayashi, H. Ohno, and H. Takahashi. Estimation of silent phenotypes of calf antibiotic dysbiosis. Scientific Reports, 13:6359, 2023.
[pdf] [Google scholar]Q. Mi, M. Chen, Z. Cai, X. Jia. What makes a readable code? A causal analysis method. Software: Practice and Experience, 1-19, 2023.
[pdf] [Google scholar]S. Arima, Y. Sasaki, S. Morie, Y. Kataoka, C. Mao, J. Lin. Data-driven Modeling for Production Dynamics. ArXiv preprint arXiv: 2301.01109, 2023.
[pdf] [Google scholar]R. Howard, L Kunze. Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour. ArXiv preprint arXiv: 2301.01109, 2023.
[pdf] [Google scholar]Y. C. Bauwelinckx, J. Dhaene, T. Verdonck, M. Heuvel. On the causality-preservation capabilities of generative modelling. ArXiv preprint arXiv: 2301.01109, 2023.
[pdf] [Google scholar]S. Lee and S. Lee. Statistical process monitoring for vector autoregressive time series based on location-scale CUSUM method. Quality Engineering, 2022.
[pdf] [Google scholar]J. Su, L. Cao, G. Lee, B. Gopaluni, L. C. Siang, Y. Cao, S. van Dyka, R. Pinchukc, J. Saddlera. Tracking the green coke production when co-processing lipids at a commercial fluid catalytic cracker (FCC): combining isotope 14C and causal discovery analysis. Sustainable Energy & Fuels, 2022.
[pdf] [Google scholar]S. Fara, O. Hickey, A. Georgescu, S. Goria, E. Molimpakis, N. Cummins. Utilising Bayesian Networks to combine multimodal data and expert opinion for the robust prediction of depression and its symptoms. ArXiv preprint arXiv:2211.04924, 2022.
[pdf] [Google scholar]Y. Uchida, K. Fujiwara, T. Saito, T. Osaka. Causal Plot: Causal-Based Fault Diagnosis Method Based on Causal Analysis. Processes, 2022.
[pdf] [Google scholar]R. Xin, P. Chen, Z. Zhao. CausalRCA: Causal Inference based Precise Fine-grained Root Cause Localization for Microservice Applications. ArXiv preprint arXiv:2209.02500, 2022.
[pdf] [Google scholar]L. Cao, J. Su, Y. Wang, Y. Cao, L. C. Siang, J. Li, J. N. Saddler, and B. Gopaluni. Causal Discovery based on Observational Data and Process Knowledge in Industrial Processes. Industrial & Engineering Chemistry Research, 2022, 2022.
[pdf] [Google scholar]G. Menegozzo, D. Dall'Alba, P. Fiorini. CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods. ArXiv preprint arXiv:2208.0152, 2022.
[pdf] [Google scholar]Y. N. Sun, W. Qin, J. H. Hu, H. W. Xu, P. Z. H. Sun. A Causal Model-Inspired Automatic Feature-Selection Method for Developing Data-Driven Soft Sensors in Complex Industrial Processes. Engineering, xx(xx): xx–xx, 2022.
[pdf] [Google scholar]R. S. Chauhan, C. Riis, S. Adhikari, S. Derrible, E. Zheleva, C. F. Choudhury, F. C. Pereira. Causality in Travel Mode Choice Modeling: A Novel Methodology that Combines Causal Discovery and Structural Equation Modeling. ArXiv preprint arXiv:2208.05624, 2022.
[pdf] [Google scholar]M. Maisonnave, F. Delbianco, F. Tohme, E. Milios, and A. G. Maguitman. Causal graph extraction from news: a comparative study of time-series causality learning techniques. PeerJ Computer Science, 8:e1066, 2022.
[pdf] [Google scholar]A. Ghosh, G. Palanichamy, D. P. Trujillo, M. Shaikh, and S. Ghosh. Insights into Cation Ordering of Double Perovskite Oxides from Machine Learning and Causal Relations. Chemistry of Materials, 34(16): 7563–7578, 2022.
[pdf] [Google scholar]M. Yan, Y.-R. Lin, W.-T. Chung. Are Mutated Misinformation More Contagious? A Case Study of COVID-19 Misinformation on Twitter. WebSci '22: 14th ACM Web Science Conference 2022, pp. 336-347, 2022.
[pdf] [Google scholar]H. Miyamoto, F. Asano, K. Ishizawa, W. Suda, H. Miyamoto, N. Tsuji, M. Matsuura, A. Tsuboi, C. Ishii, T. Nakaguma, C. Shindo, T. Kato, A. Kurotani, H. Shima, S. Moriya, M. Hattori, H. Kodama, H. Ohno, J. Kikuchi. A potential network structure of symbiotic bacteria involved in carbon and nitrogen metabolism of wood-utilizing insect larvae. Science of The Total Environment, 836(25): 155520, 2022.
[pdf] [Google scholar]T. Uchida, K. Fujiwara, K. Nishioji, M. Kobayashi, M. Kano, Y. Seko, K. Yamaguchi, Y. Itoh, H. Kadotanie. Medical checkup data analysis method based on LiNGAM and its application to nonalcoholic fatty liver disease. Artificial Intelligence in Medicine, 128: 102310, 2022.
[pdf] [Google scholar]高山正行, 小柴等, 前田高志ニコラス, 三内顕義, 清水昌平, 星野利彦. 博士課程進学率に関する因果モデルの構築. Jxiv, JST プレプリントサーバ, 2022.
[pdf] [Google scholar]A. Ghosh, G. Palanichamy, D. P. Trujillo, S. Ghosh. Insights of cation ordering in double perovskites oxides from machine learning. ArXiv preprint arXiv:201.04970, 2022.
[pdf] [Google scholar]Y. Liu, M. Ziatdinov, S. V. Kalinin. Exploring causal physical mechanisms via non-gaussian linear models and deep kernel learning: applications for ferroelectric domain structures. ArXiv preprint arXiv:2110.06888.
[pdf] [Google scholar]L. Wu, J. Tordsson, E. Elmroth, O. Kao. Causal Inference Techniques for Microservice Performance Diagnosis: Evaluation and Guiding Recommendations. ACSOS 2021 - 2nd IEEE International Conference on Autonomic Computing and Self-Organizing Systems, pp. xx-xx., Washington DC, United States, 2021.
[pdf] [Google scholar]R. Jarry, S. Kobayashi, K. Fukuda. A Quantitative Causal Analysis for Network Log Data. 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), pp. xx-xx., Madrid, Sparin, 2021.
[pdf] [Google scholar]C. Nelson , M. Ziatdinov , X. Zhang , R. Vasudevan , E. Eliseev , A. Morozovska, I. Takeuchi and S. Kalinin. Causal Analysis of Parameterized Atomic HAADF-STEM Across a Doped Ferroelectric Phase Boundary. Microscopy and Microanalysis, 27(S1), 2762-2764.
[pdf] [Google scholar]R. Giriraj, S. S. Thomas. Causal Discovery in Knowledge Graphs by Exploiting Asymmetric Properties of Non-Gaussian Distributions. Arxiv preprint arXiv:2106.01043, 2021.
[pdf] [Google scholar]L. Wu, J. Tordsson, J. Bogatinovski, E. Elmroth, O. Kao. MicroDiag: Fine-grained Performance Diagnosis for Microservice Systems. ICSE21 Workshop on Cloud Intelligence, pp. xx-xx, Madrid, Spain, 2021. hal-03155797
[pdf] [Google scholar]C. Nelson, A. N. Morozovska, M. A. Ziatdinov, E. A. Eliseev, X. Zhang, I. Takeuchi, S. V. Kalinin. Mapping causal patterns in crystalline solids. Arxiv preprint arXiv:2103.01951,2021.
[pdf] [Google scholar]大山 飛鳥, 古徳 純一, 土岐 博. 大阪府の特定健康診査データの因果探索. 情報処理, 63(2): 19-47, 2022.
[pdf] [Google scholar]J. Kotoku , A. Oyama, K. Kitazumi, H. Toki, A. Haga, R. Yamamoto, M. Shinzawa, M. Yamakawa, S. Fukui, K. Yamamoto, T. Moriyama. Causal relations of health indices inferred statistically using the DirectLiNGAM algorithm from big data of Osaka prefecture health checkups. PLoS ONE,15(12): e0243229, 2020.
[pdf] [Google scholar]M. O. Gani, S. Kethireddy, M. Bikak, P. Griffin, M. Adibuzzaman. Structural Causal Model with Expert Augmented Knowledge to Estimate the Effect of Oxygen Therapy on Mortality in the ICU. CRITICAL CARE, 15(4): xx-xx, 2020.
[pdf] [Google scholar]J. Liu, D. Niyogi. Identification of linkages between urban heat Island magnitude and urban rainfall modification by use of causal discovery algorithms. Urban Climate, 33: xx-xx, 2020.
[pdf] [Google scholar]K. Meding, D. Janzing, B. Schölkopf, F. A. Wichmann. Perceiving the arrow of time in autoregressive motion. In Advances in Neural Information Processing Systems 33 (NIPS2019), pp. xx-xx, 2019.
[pdf] [Google scholar]P. J. Banks, J. C. Banks. Relationship between soil and groundwater salinity in the Western Canada Sedimentary Basin. Environmental Monitoring and Assessment, xx: xx-xx, 2019.
[pdf] [Google scholar]X. Chen, J. Wang, and J. Zhou. Process monitoring based on multivariate causality analysis and probability inference. IEEE Access, xx: xx-xx, 2018.
[pdf] [Google scholar]O. A. Guerrero and G. Castaneda. Evaluating Policy Priorities under Social Learning and Endogenous Government Behavior. SSRN, 2018.
[pdf] [Google scholar]P. Hu, R. Jiao, L. Jin, and M. Xiong. Application of causal inference to genomic analysis: advances in methodology. Frontiers in Genetics, 9: 238, 2018.
[pdf] [Google scholar]O. A. Guerrero and G. Castaneda. Quantifying the Coherence of Development Policy Priorities. SSRN, 2018.
[pdf] [Google scholar]M. Kondo, O. Mizuno, and E.-H. Choi. Causal-Effect analysis using Bayesian LiNGAM comparing with correlation analysis in function point metrics and effort. International Journal of Mathematical, Engineering and Management Sciences, xx: xx-xx, 2018.
[pdf] [Google scholar]T. N. Maeda, J. Mori, M. Ochi, T. Sakimoto, and I. Sakata. Measurement of Opportunity Cost of Travel Time for Predicting Future Residential Mobility Based on the Smart Card Data of Public Transportation. Preprints 2018, 2018080389, 2018.
[pdf] [Google scholar]L. C. Parra and L. Hirsch. Award or reward? Which comes first, NIH funding or research impact?. bioRxiv 193755, 2017.
[pdf] [Google scholar]S. Louvigné, M. Uto, Y. Kato, and T. Ishii. Social constructivist approach of motivation: social media messages recommendation system. Behaviormetrika, xx: xx-xx, 2017.
[pdf] [Google scholar]Y. Zhang, Y. Cen, and G. Luo. Causal direction inference for network alarm analysis. Control Engineering Practice, 70: 148-153, 2018.
[pdf] [Google scholar]M. Kondo and O. Mizuno. Analysis on causal-effect relationship in effort metrics using Bayesian LiNGAM. In Proc. 2016 IEEE 27th International Symposium on Software Reliability Engineering Workshops (ISSREW), pp. xx-xx, Ottawa, Ontario, Canada, 2016.
[pdf] [Google scholar]B. Tepeš, G. Lešin, A. Hrkac, Krunoslav Tepeš. Causal Bayes model of mathematical competence in kindergarten. Journal on Systemics, Cybernetics and Informatics, 14(3): 14-17, 2016.
[pdf] [Google scholar]M. Palma, Y. Li, D. Vedenov, and D. Bessler. The order of variables, simulation noise and accuracy of mixed logit estimates. No 235990, 2016 Annual Meeting, July 31-August 2, 2016, Boston, Massachusetts from Agricultural and Applied Economics Association, 2016.
[pdf] [Google scholar]Y. Hu, K. Liu, X. Zhang, K. Xie, W. Chen, Y. Zeng, and M. Liu. Concept drift mining of portfolio selection factors in stock market. Electronic Commerce Research and Applications, xx(xx):xx-xx, 2015.
[pdf] [Google scholar]M. Ballings, D. Van den Poel, and M. Bogaert. Social media optimization: Identifying an optimal strategy for increasing network size on Facebook. Omega, xx(xx):xx-xx, 2015.
[pdf] [Google scholar]R. Cai, C. Yuan, Z. Hao, W. Wen, L. Wang, W. Chen and Z. Li. A causal model for disease pathway discovery. In Proc. 21th Int. Conf. on Neural Information Processing (ICONIP2014), pp. 350-357, Kuching, Malaysia, 2014.
[pdf] [Google scholar]L. Pickup, Z. Pan, D. Wei, Y. Shih, C. Zhang, A. Zisserman, B. Schölkopf, and W. Freeman. Seeing the arrow of time. Proc. 2014 IEEE Conference on Computer Vision and Patten Recognition (CVPR), pp. 2043 - 2050, Columbus, OH, USA, 2014.
[pdf] [Google scholar]S. Saleh, M. Raja, M. Shahnawaz, M. U. Ilyas, K. Khurshid, M. Z. Shafiq, A. X. Liu, H. Radha and S. Karande. Breaching IM session privacy using causality. In Proc. IEEE Global Communications Conference 2014 (GLOBECOM2014), pp. 686-691, Austin, TX, USA, 2014.
[pdf] [Google scholar]Z. Zhang, Y. Inoue, K. Ikeda, T. Shibata, T. Bandou and T. Miyahara. Abnormal driving behavior detection using a linear non-Gaussian acyclic model for causal discovery. In Proc. FISITA 2012 World Automotive Congress, pp. 529-536, Beijing, China, 2013.
[pdf] [Google scholar]A. Statnikov, M. Henaff, N. I. Lytkin and C. F. Aliferis. New methods for separating causes from effects in genomics data. BMC Genomics, 13(Suppl 8): S22, 2012.
[pdf] [real data] [Google scholar]G. Netuveli and M. Bartley. Perception is reality: Effect of subjective versus objective socio-economic position on quality of life. Sociology, 45(6): 1208-1215, 2012.
[pdf] [Google scholar]K. Ozaki, H. Toyoda, N. Iwama, S. Kubo and J. Ando Using non-normal SEM to resolve the ACDE model in the classical twin design. Behavior Genetics, 41(2): 329--339, 2011.
[pdf] [Google scholar]D. Niyogi, C. Kishtawal, S. Tripathi and R. S. Govindaraju. Observational evidence that agricultural intensification and land use change may be reducing the Indian summer monsoon rainfall. Water Resources Research, 46, 2010.
[pdf] [Google scholar]K. Ozaki and J. Ando. Direction of causation between shared and non-shared environmental factors. Behavior Genetics, 39(3): 321--336, 2009.
[pdf] [Google scholar]S. Imoto, T. Higuchi, T. Goto, K. Tashiro, S. Kuhara and S. Miyano. Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks. Journal of Bioinformatics and Computational Biology, 2(1):77-98, 2004.
[pdf] [Google scholar]
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