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Causal Inference Applied to Explaining the Appearance of Shadow Phenomena in an Image
Volume 34, Issue 3 (2023), pp. 665–677
Pub. online: 1 September 2023      Type: Research Article      Open accessOpen Access
Received
1 April 2023
Accepted
1 August 2023
Published
1 September 2023

Abstract

Due to the complexity and lack of transparency of recent advances in artificial intelligence, Explainable AI (XAI) emerged as a solution to enable the development of causal image-based models. This study examines shadow detection across several fields, including computer vision and visual effects. Three-fold approaches were used to construct a diverse dataset, integrate structural causal models with shadow detection, and apply interventions simultaneously for detection and inferences. While confounding factors have only a minimal impact on cause identification, this study illustrates how shadow detection enhances understanding of both causal inference and confounding variables.

References

 
Ankan, A., Panda, A. (2015). pgmpy: probabilistic graphical models using python. In: Proceedings of the 14th Python in Science Conference (SCIPY 2015). Citeseer.
 
Beaumont, P., Horsburgh, B., Pilgerstorfer, P., Droth, A., Oentaryo, R., Ler, S., Nguyen, H., Ferreira, G.A., Patel, Z., Leong, W. (2021). CausalNex. https://github.com/quantumblacklabs/causalnex.
 
Bradski, G. (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools, 120, 122–125.
 
Chiappa, S., Isaac, W.S. (2019). A causal Bayesian networks viewpoint on fairness. In: Kosta, E., Pierson, J., Slamanig, D., Fischer-Hübner, S., Krenn, S. (Eds.), Privacy and Identity Management. Fairness, Accountability, and Transparency in the Age of Big Data. Privacy and Identity 2018, IFIP Advances in Information and Communication Technology, Vol. 547. Springer, Cham. https://doi.org/10.1007/978-3-030-16744-8_1.
 
Dague, L., Lahey, J.N. (2019). Causal inference methods: lessons from applied microeconomics. Journal of Public Administration Research and Theory, 29(3), 511–529. https://doi.org/10.1093/jopart/muy067.
 
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L. (2009). ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. https://doi.org/10.1109/CVPR.2009.5206848.
 
Fire, A., Zhu, S.-C. (2013). Using causal induction in humans to learn and infer causality from video. Proceedings of the Annual Meeting of the Cognitive Science Society, 35, 2297–2302. https://escholarship.org/uc/item/4ng247kx.
 
Guo, R., Cheng, L., Li, J., Hahn, P.R., Liu, H. (2020). A survey of learning causality with data: problems and methods. ACM Computing Surveys, 53(4), 1–37. https://doi.org/10.1145/3397269.
 
He, Y.B., Geng, Z. (2008). Active learning of causal networks with intervention experiments and optimal designs. Journal of Machine Learning Research, 9, 2523–2547.
 
Hernán, M.A., Robins, J.M. (2020). Causal Inference: What If, I ed. Chapman & Hall/CRC, Boca Raton, pp. 312. 978-1420076165. https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/.
 
Lebeda, K., Hadfield, S., Bowden, R. (2015). Exploring causal relationships in visual object tracking. In: 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015, pp. 3065–3073. https://doi.org/10.1109/ICCV.2015.351.
 
Levoy, M. (1990). Efficient ray tracing of volume data. ACM Transactions on Graphics (TOG), 9(3), 245–261. https://doi.org/10.1145/78964.78965.
 
Li, T.-M., Aittala, M., Durand, F., Lehtinen, J. (2018). Differentiable Monte Carlo ray tracing through edge sampling. ACM Transactions on Graphics, 37(6). 1–11. https://doi.org/10.1145/3272127.3275109.
 
Lopez-Paz, D., Nishihara, R., Chintala, S., Sch¨lkopf, B., Bottou, L. (2017). Discovering causal signals in images. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 58–66. https://doi.org/10.1109/CVPR.2017.14.
 
Martin, O. (2018). Bayesian Analysis with Python, 2nd ed. Packt Publishing, Birmingham, Mumbai. 978-1-78588-380-4.
 
Murphy, K.P. (2012). Machine Learning: A Probabilistic Perspective, Vol. 16. The MIT Press, Massachusetts, pp. 1–1098. 978-0-262-01802-9.
 
Pearl, J., Mackenzie, D. (2018). The Book of Why, I ed. Basic Books, New York. 0465097618.
 
Pearl, J., Glymour, M., Jewell, N.P. (2019). Causal Inference in Statistics A Primer. John Wiley & Sons Ltd, West Sussex, pp. 159. 9781119186847.
 
Perry, M.J. (2003). Causality: models, reasoning, and inference. In: The Encyclopedia of Ancient History, Vol. 19, pp. 675–685. https://doi.org/10.1002/9781444338386.wbeah13034.
 
Pickup, L.C., Pan, Z., Wei, D., Shih, Y., Zhang, C., Zisserman, A., Scholkopf, B., Freeman, W.T. (2014). Seeing the arrow of time. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014, pp. 2043–2050. https://doi.org/10.1109/CVPR.2014.262.
 
Saeed, W., Omlin, C. (2021). Explainable AI (XAI): a systematic meta-survey of current challenges and future opportunities. Knowledge-Based Systems, 263, 110273. https://doi.org/10.1016/j.knosys.2023.110273.
 
van der Walt, S., Schönberger, J.L., Nunez-Iglesias, J., Boulogne, F., Warner, J.D., Yager, N., Gouillart, E., Yu, T. (2014). scikit-image: image processing in Python. PeerJ, 2, e453. https://doi.org/10.7717/peerj.453.
 
Wong, L. (2016). Causalinference. https://causalinferenceinpython.org/.
 
Xin, Y., Tagasovska, N., Perez-Cruz, F., Raubal, M. (2022). Vision paper: causal inference for interpretable and robust machine learning in mobility analysis. In: SIGSPATIAL’22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3557915.3561473.
 
Zeiler, M.D., Fergus, R. (2014). Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (Eds.), Computer Vision – ECCV 2014, Lecture Notes in Computer Science, Vol. 8689. Springer, Cham, pp. 818–833. https://doi.org/10.1007/978-3-319-10590-1_53.
 
Zheng, X., Aragam, B., Ravikumar, P., Xing, E.P. (2018). Dags with NO TEARS: continuous optimization for structure learning. In: NIPS’18: Proceedings of the 32nd International Conference on Neural Information Processing Systems, 2018 December, pp. 9472–9483.

Biographies

Vélez Bedoya Jairo Ivan
https://orcid.org/0000-0001-8756-1561
jairo.velez@ucaldas.edu.co

J.I. Vélez Bedoya has a master’s degree in software project management and development (2011) from the Autónoma University of Manizales in Colombia. He is currently a PhD candidate in systems engineering and informatics at the University of Zaragoza in Spain. He belongs to the Systems and Informatics Department at the University of Caldas, where he is a full time professor since 2017.

González Bedia Manuel Andres
https://orcid.org/0000-0002-8263-2444
mgbedia@unizar.es

M.A. González Bedia is a postdoctoral researcher from University of Sussex (2009), he has a PhD in computer science from the University of Salamanca (Spain). He is a full time professor at the Department of Computer Science and Systems Engineering of the University of Zaragoza in Spain (since 2008) and also he is a researcher at the Institute of Engineering Research of Aragon (I3A) (since 2012), from 2019 he has been Science and Universities Advisor for the Ministry of Science, Innovation and Universities of the Spanish government, Deputy Director General of University Research Activity Ministry of Universities of the Spanish government (since 2020) and Director General, Commissioner for the New Language Economy at the Ministry of Economic Affairs and Digital Transformation of the Spanish government (since 2022).

Castillo Ossa Luis Fernando
https://orcid.org/0000-0002-2878-8229
luis.castillo@ucaldas.edu.co

L.F. Castillo Ossa has a PhD in computer science and automation from University of Salamanca (Spain) and was the dean of the Faculty of Engineering at the University of Caldas (Colombia) from 2014 until 2018 where he also works as professor in the Department of Systems and Informatics. He is the head of the Research Group Artificial Intelligence, senior researcher in MinCiencias. His main lines of research are related to multiagent systems and artificial cognitive systems. He works part-time as a professor in the National University of Colombia, Campus Manizales, professor of doctorate in Cognitive Science Autonomous University, Manizales. He belongs to the Systems and Informatics Department at the University of Caldas where he is a full time professor since 2011.

Arango López Jeferson
https://orcid.org/0000-0001-8072-9130
jeferson.arango@ucaldas.edu.co

J. Arango López received his MSc in computational engineering from the University of Caldas in Colombia. He obtained his PhD in electronic science from the University of Cauca in Colombia and PhD in communication and information technologies from the University of Granada in Spain (2019). He is currently full-time professor at the University of Caldas (Colombia) and his research interests are pervasive games, semantic web, artificial intelligence and linked open data.

Moreira Fernando
https://orcid.org/0000-0002-0816-1445
afmoreira@upt.pt

F. Moreira has a MSc in electronic engineering (1997) and PhD in electronic engineering (2003), both from the Faculty of Engineering of the University of Porto and Habilitation (2018). He has been a member of the Science and Technology Department at Portucalense University since 1992, currently as a full-time professor, and a visiting professor at the University of Porto Business School.


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© 2023 Vilnius University
Open access article under the CC BY license.

Keywords
causality causal inference causal discovery structural causal model shadow detection XAI

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