References

Abbott, S. (2015). Understanding analysis. Springer.

Capiński, M., & Kopp, P. E. (2004). Measure, integral and probability (Vol. 14). Springer.

Hernán, M. A., & Robins, J. M. (2010). Causal inference. CRC Boca Raton, FL.

Kennedy, E. H. (2022). Semiparametric doubly robust targeted double machine learning: A review. arXiv Preprint arXiv:2203.06469.

Kreyszig, E. (1991). Introductory functional analysis with applications (Vol. 17). John Wiley & Sons.

Laan, M. J., & Robins, J. M. (2003). Unified methods for censored longitudinal data and causality. Springer.

Petersen, M. L., & Laan, M. J. van der. (2014). Causal models and learning from data: Integrating causal modeling and statistical estimation. Epidemiology (Cambridge, Mass.), 25(3), 418.

Richardson, T. S., & Robins, J. M. (2013). Single world intervention graphs (SWIGs): A unification of the counterfactual and graphical approaches to causality. Center for the Statistics and the Social Sciences, University of Washington Series. Working Paper, 128(30), 2013.

Tsiatis, A. A. (2006). Semiparametric theory and missing data.

Van der Laan, M. J., Rose, S., et al. (2011). Targeted learning: Causal inference for observational and experimental data (Vol. 4). Springer.

Van der Vaart, A. W. (2000). Asymptotic statistics (Vol. 3). Cambridge university press.