Applications and tailor-made methods
Applications and tailor-made methods
Neuroscience
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 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]NEW 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
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
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 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]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.
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