Hein, M. and Bousquet, O. (2004). Kernels, associated structures and generalizations.
Hernan, M. and Robins, J. (2020). Causal inference: What if. boca raton: Chapman & hall/crc.
Hill, J. L. (2011). Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics, 20(1):217-240.
Horváth, L. and Kokoszka, P. (2012). Inference for functional data with applications, volume 200. Springer Science & Business Media.
Hsing, T. and Eubank, R. (2015). Theoretical foundations of functional data analysis, with an introduction to linear operators, volume 997. John Wiley & Sons.
Imbens, G. W. (2004). Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and Statistics, 86(1):4-29.
Johansson, F., Shalit, U., and Sontag, D. (2016). Learning representations for counterfactual inference. In International Conference on Machine Learning, pages 3020-3029. PMLR.
Kang, J. D. Y. and Schafer, J. L. (2007). Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data. Statistical Science, 22(4):523 - 539.
Kennedy, E. H. (2019). Nonparametric causal effects based on incremental propensity score interventions. Journal of the American Statistical Association, 114(526):645-656.
Kennedy, E. H. (2022). Semiparametric doubly robust targeted double machine learning: a review. arXiv preprint arXiv:2203.06469.
Kennedy, E. H., Balakrishnan, S., and G'Sell, M. (2020). Sharp instruments for classifying compliers and generalizing causal effects. The Annals of Statistics, 48(4):2008-2030.
Kennedy, E. H., Balakrishnan, S., and Wasserman, L. (2021). Semiparametric counterfactual density estimation. arXiv preprint arXiv:2102.12034.
Kim, I. and Ramdas, A. (2023). Dimension-agnostic inference using cross U-statistics. Bernoulli.
Kim, K., Kim, J., and Kennedy, E. H. (2018). Causal effects based on distributional distances. arXiv preprint arXiv:1806.02935.
Landmesser, J. M. (2016). Decomposition of differences in income distributions using quantile regression. Statistics in Transition new series, 17(2):331-348.
Li, L., Tchetgen, E. T., van der Vaart, A., and Robins, J. M. (2011). Higher order inference on a treatment effect under low regularity conditions. Statistics & Probability Letters, 81(7):821-828.
Louizos, C., Shalit, U., Mooij, J. M., Sontag, D., Zemel, R., and Welling, M. (2017). Causal effect inference with deep latent-variable models. Advances in Neural Information Processing Systems, 30.
Luedtke, A. R., Diaz, I., and van der Laan, M. J. (2015). The statistics of sensitivity analyses.
Muandet, K., Kanagawa, M., Saengkyongam, S., and Marukatat, S. (2021). Counterfactual mean embeddings. The Journal of Machine Learning Research, 22(1):7322-7392.
Muñoz, I. D. and Van Der Laan, M. (2012). Population intervention causal effects based on stochastic interventions. Biometrics, 68(2):541-549.
Park, J. and Muandet, K. (2023). Towards empirical process theory for vector-valued functions: Metric entropy of smooth function classes. In International Conference on Algorithmic Learning Theory, pages 1216-1260. PMLR.
Park, J., Shalit, U., Scholkopf, B., and Muandet, K. (2021). Conditional distributional treatment effect with kernel conditional mean embeddings and U-statistic regression. In International Conference on Machine Learning, pages 8401-8412. PMLR.