Applications and tailor-made methods
Applications and tailor-made methods
Neuroscience
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: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]D. Takahashi, S. Shimizu, and T. Tanaka. Counterfactual explanations of black-box machine learning models using causal discovery with applications to credit rating. In Proc. Int. Joint Conf. on Neural Networks (IJCNN2024), part of the 2024 IEEE World Congress on Computational Intelligence (WCCI2024), pages xx--xx, Yokohama, Japan, 2024. Accepted.
[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
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 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]NEW 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]NEW 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]NEW 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]NEW 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]NEW 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.
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