Blog

by Johan de Aguas

Semiparametric estimation for the mean ReLU

A simple setting for a nonregular inference problem

A semi-formal presentation of targeted minimum loss estimation (TMLE)

TMLE combines ideas from targeted learning and the efficient influence function to provide mutiply-robust and efficient estimation method for causal inference

Simulation task: recovering causal effects from post-treatment selection induced by missing outcome data

A simulation exercise on missing data, selection bias, causal inference and TMLE

Structure learning for downstream causal inference with missing outcome data

causal inference

graphical models

structure learning

A set of ideas on how to leverage structure learning and graph uncertainty quantification for downstream causal inference with missing data mechanisms in the outcome variable

Uncertainty modeling and quantification for causal inference

causal inference

graphical models

structure learning

A theoretical introduction of UQ in causal inference

Simulation task: recovering causal effects from post-treatment selection induced by missing outcome data

A simulation exercise on missing data, selection bias, causal inference and TMLE

Recovering causal effects from post-treatment selection induced by missing outcome data

Solutions to the problem of effect recoverability from selection/missingness via regression adjustment and doubly-robust estimation

Recovering from selection bias with IPW methods

On the consistency of IPW methods to recover causal effects from selection bias