iamrp.net-M0qM-1791491022-z532465700 1791491022 Institute for Advanced Materials Research Press info@iamrp.net Institute for Advanced Materials Research Press Journal of Computational and Data-Driven Materials Engineering J. Comput. Data-Driven Mater. Eng. 3149-9368 2025 4 2 George Brown Michael Taylor Sarah Wilson Olivia Harris 2025 10.68159/z532465700 https://iamrp.net/pub/journal/2/article/z532465700 Li Z, Yoon J, Zhang R, Rajabipour F, Srubar WV 3rd, Dabo I, et al. Machine learning in concrete science: Applications, challenges, and best practices. NPJ Comput Mater. 2022;8(1):127. 10.1038/s41524-022-00810-x Pilania G. Machine learning in materials science: From explainable predictions to autonomous design. Comput Mater Sci. 2021;193:110360. 10.1016/j.commatsci.2021.110360 Ziatdinov M, Nelson CT, Zhang X, Vasudevan RK, Eliseev E, Morozovska AN, et al. Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution scanning transmission electron microscopy data. NPJ Comput Mater. 2020;6(1):127. 10.1038/s41524-020-00396-2 Sun X, Bahmani B, Vlassis NN, Sun W, Xu Y. Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation. Granul Matter. 2022;24(1):1. 10.1007/s10035-021-01137-y Tian X, Bai J, Gong X, Lei R, Yan Y. Leveraging causal discovery and causal inference to improve interpretability of Charpy impact toughness. Comput Mater Sci. 2025;259:114164. 10.1016/j.commatsci.2025.114164 Ting JY, Barnard AS. Data-driven causal inference of process-structure relationships in nanocatalysis. Curr Opin Chem Eng. 2022;36:100818. 10.1016/j.coche.2022.100818 Liu R, Hao J, Li J, Wang S, Liu H, Zhou Z, et al. Causal inference machine learning leads original experimental discovery in CdSe/CdS core/shell nanoparticles. J Phys Chem Lett. 2020;11(17):7232-8. 10.1021/acs.jpclett.0c02115 Seike K, Manaka H, Miura Y. Causal inference in statistics insights into stress-induced ferroelectric states in SrTiO3: Disentangling piezoelectric and flexoelectric effects from birefringence images. Sci Technol Adv Mater Methods. 2025;5(1):2503698. 10.1080/27660400.2025.2503698 Hu J, Liu D, Fu N, Dong R. Realistic material property prediction using domain adaptation based machine learning. Digit Discov. 2024;3(2):300-12. 10.1039/D3DD00162H Soares E, Shirasuna VY, Brazil EV, Aquino Gutierrez KF, Cerqueira R, Zubarev D, et al. Causality-driven feature selection and domain adaptation for enhancing chemical foundation models in downstream tasks. Mach Learn Sci Technol. 2025;6(1):015017. 10.1088/2632-2153/adabb1 Kamarthi A, Kaliyamoorthy B. Causal inference-informed explainable selective weighted ensemble for predicting ultra-high-performance concrete compressive strength. Knowl Based Syst. 2025;330:114770. 10.1016/j.knosys.2025.114770 Zhao YM, Zhang JY, Liaw PK, Yang T. Machine learning-based computational design methods for high-entropy alloys. High Entropy Alloys Mater. 2025;3(1):41-100. 10.1007/s44210-025-00055-5 Loh WW. Causal inference with unobserved confounding: Leveraging negative control outcomes using lavaan. Multivar Behav Res. 2025;60(5):1029-41. 10.1080/00273171.2025.2507742 Kallus N, Zhou A. Minimax-optimal policy learning under unobserved confounding. Manag Sci. 2021;67(5):2870-90. 10.1287/mnsc.2020.3699 Zang Y, Koutsourelakis PS. PSP-GEN: Stochastic inversion of the process-structure-property chain in materials design through deep, generative probabilistic modeling. Acta Mater. 2025;284:120600. 10.1016/j.actamat.2024.120600 Kong D, Yang S, Wang L. Identifiability of causal effects with multiple causes and a binary outcome. Biometrika. 2022;109(1):265-72. 10.1093/biomet/asab016 Miller LC, Shaikh SJ, Jeong DC, Wang L, Gillig TK, Godoy CG, et al. Causal inference in generalizable environments: Systematic representative design. Psychol Inq. 2019;30(4):173-202. 10.1080/1047840X.2019.1693866 Hahn J, Ridder G. Instrumental variable estimation of nonlinear models with nonclassical measurement error using control variables. J Econom. 2017;200(2):238-50. 10.1016/j.jeconom.2017.06.008 Plagborg-Møller M, Wolf CK. Instrumental variable identification of dynamic variance decompositions. J Polit Econ. 2022;130(8):2164-202. 10.1086/720141 Xie Z, Chen Z, Jiang Y, Yao J, Zhang P, Lei H, et al. Causal relationships between epilepsy and the microstructure of the white matter: A Mendelian randomization study. Medicine (Baltimore). 2024;103(44):e40090. 10.1097/MD.0000000000040090 Chen D, Zhao H, He J, Pan Q, Zhao W. An causal XAI diagnostic model for breast cancer based on mammography reports. In: Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); 2021 Dec 9-12; Virtual. Piscataway (NJ): IEEE; 2021. p. 3341-9. 10.1109/BIBM52615.2021.9669648 Barakati K, Molak A, Nelson C, Zhang X, Takeuchi I, Kalinin SV. Causal discovery from data assisted by large language models. Appl Phys Lett. 2025;127(12):121904. 10.1063/5.0272287 Mogensen SW, Rathsman K, Nilsson P. Causal discovery in a complex industrial system: A time series benchmark. In: Locatello F, Didelez V, editors. Proceedings of the Third Conference on Causal Learning and Reasoning. Proc Mach Learn Res. 2024;236:1218-36. Shen X, Raghavan S, Przybelski SA, Lesnick TG, Ma S, Reid RI, et al. Causal structure discovery identifies risk factors and early brain markers related to evolution of white matter hyperintensities. Neuroimage Clin. 2022;35:103077. 10.1016/j.nicl.2022.103077 Cai S, Peng H, Liu R, Chen P. Causal-oriented representation learning for time-series forecasting based on the spatiotemporal information transformation. Commun Phys. 2025;8:242. 10.1038/s42005-025-02170-6 Castri L, Mghames S, Hanheide M, Bellotto N. CAnDOIT: Causal discovery with observational and interventional data from time series. Adv Intell Syst. 2024;6(12):2400181. 10.1002/aisy.202400181 Ni B, Glaser B, Taheri-Mousavi SM. End-to-end prediction and design of additively manufacturable alloys using a generative AlloyGPT model. NPJ Comput Mater. 2025;11(1):294. 10.1038/s41524-025-01768-2 Ahuja K, Mahajan D, Wang Y, Bengio Y. Interventional causal representation learning. In: Krause A, Brunskill E, Cho K, Engelhardt B, Sabato S, Scarlett J, editors. Proceedings of the 40th International Conference on Machine Learning. Proc Mach Learn Res. 2023;202:372-407. Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA. Generative adversarial networks: An overview. IEEE Signal Process Mag. 2018;35(1):53-65. 10.1109/MSP.2017.2765202