I use these notes as a way to make sure I understand the basic concepts of each topic. I may use them as supplementary materials of graduate courses that I will be teaching. Comments and suggestions are welcome.
A probabilistic introduction to missing data.
A short note on nonparametric additive models.
A short note on normalizing flows.
A short note on marginal structural models with continuous treatments.
A short note on the benign overfitting.
A short note on weighted average derivative effect.
A short note on reducing variance by estimating the nuisance.
A short note on instrumental variables.
A short note on the algorithmic fairness: demographic parity.
A short note on the proximal causal inference.
A short note on the information bounds of generalization errors.
A short note on decoupling the efficiency and missing/causal assumption.
A short note on quantile classifiers.
A short note on the algorithmic fairness.
A short note on the kernel VC-type condition.
A short note on conditional models, robustness, and missingness.
A short note on the median-of-means estimator.
A short note on the Bernstein-von Mises theorem under infinite dimensions.
A short note on the causality and its minimax framework.
A short note on the linear form of the Cox model estimator.
A short note on the empirical likelihood and calibration.
A short note on the Frequentist consistency of a nonparametric Bayes.
A short note on the experimental design.
A short note on the adjustment sets.
A short note on the variational inference.
A short note on the Rasch model and latent trait model.
A short note on the mixture of experts.
A short note on the constrained nonparametric quantile regression.
A short note on the effect mediation.
Density and the variance of a nonparametric estimator (10/04/16).
A great review on the Topological Data Analysis by Larry Wasserman (10/01/16).
Recommended books for learning nonparametric statistics (09/30/16).
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