A list of videos, books and paper about Sensitivity analysis

There are several books about the topic.

• Borgonovo, Emanuele. Sensitivity Analysis: An Introduction for the Management Scientist. 2017. Springer International Publishing. ISBN 978-3-319-52259-3. https://www.springer.com/gp/book/9783319522579

  • This book is an introduction to the methodology of sensitivity analysis of model output. Primarily intended for people that are familiar with mathematical models but less familiar with the techniques for performing sensitivity analysis.

• From Cacuci

  • Cacuci, D. G., Ionescu-Bujor, M., & Navon, I. M. (2005). Sensitivity and uncertainty analysis, volume II: applications to large-scale systems (Vol. 2). CRC press.
  • [Paper] Cacuci, D. G., & Ionescu-Bujor, M. (2004). A comparative review of sensitivity and uncertainty analysis of large-scale systems—II: statistical methods. Nuclear science and engineering, 147(3), 204-217.
  • Cacuci, D. G. Sensitivity and Uncertainty Analysis: Theory. Volume 1. (2003).

• From Saltelli

  • Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., ... & Tarantola, S. (2008). Global sensitivity analysis: the primer. John Wiley & Sons.
  • Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity analysis in practice: a guide to assessing scientific models (Vol. 1). New York: Wiley.
  • Saltelli, A., & Chan, K. Sensitivity analysis: gauging the worth of scientific models. 2000. New York: Wiley.

• Yeung, D. S., Cloete, I., Shi, D., & wY Ng, W. Sensitivity analysis for neural networks. (2010). Springer-Verlag Berlin Heidelberg. ISBN 978-3-642-02531-0. https://www.springer.com/gp/book/9783642025310

  • This book is a good help for researchers working with neural networks and machine learning.

• Insua, D. R. Sensitivity analysis in multi-objective decision making. In Sensitivity Analysis in Multi-objective Decision Making (1990). Springer, Berlin, Heidelberg. ISBN 978-3-642-51656-6. https://www.springer.com/gp/book/9783540526926

• NIST/SEMATECH e-Handbook of Statistical Methods. https://www.itl.nist.gov/div898/handbook/ or https://doi.org/10.18434/M32189.

  • A large collection of statistics methods. Well organized, and covers lots of topics, including statistical analysis. The full handbook can be downloaded as a pdf file here.

• SHAP (SHapley Additive exPlanations) values

  • Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in neural information processing systems (pp. 4765-4774). http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions
  • Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., ... & Lee, S. I. (2020). From local explanations to global understanding with explainable AI for trees. Nature machine intelligence, 2(1), 2522-5839. https://rdcu.be/b0z70
  • Lundberg, Scott M., et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nature biomedical engineering 2.10 (2018): 749-760. https://rdcu.be/baVbR

• Bayesian models

  • Liew, S. X., Afrasiabi, M., & Austerweil, J. L. (2019). An introduction to data analysis using the PyMC3 probabilistic programming framework: A case study with Gaussian Mixture Modeling.
  • Volpatto, D., Oliveira, L. V., Bittencourt, S. P., Silva, D., & Manoel, E. (2020). Sensitivity analysis, calibration and uncertainty quantification for heavy oil viscosity estimates with a corresponding states model.
  • Jacobs, C. (2020). Sensie: Probing the sensitivity of neural networks. Journal of Open Source Software, 5(50), 2180.

• https://github.com/jphall663/awesome-machine-learning-interpretability
  • a very good list of machine learning interpretability resources.
• https://github.com/jnikhilreddy/Explainable-AI-papers-Year-wise
  • a list of papers in the area of Explainable Artificial Intelligence from 2014 up to 2020 (when I last checked).