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Causal Inference for Continuous Multiple Time Point Interventions

Recoverability of Causal Effects in a Longitudinal Study under Presence of Missing Data

Causal Fair Machine Learning via Rank-Preserving Interventional Distributions

Doubly Robust Estimation of Average Treatment Effects on the Treated through Marginal Structural Models

Introduction to Statistics and Data Analysis - With Exercises, Solutions and Applications in R (Second updated and extended edition)

Our introcutory book contains a comprehensive and thorough overview of fundamental statistical concepts such as hypothesis testing, inference and regression; and each chapter is accompanied by a wealth of exercises and solutions.

The Delta-Method and Influence Function in Medical Statistics: a Reproducible Tutorial

Estimating the Effect of Central Bank Independence on Inflation Using Longitudinal Targeted Maximum Likelihood Estimation

Recently, there has been a lot of interest and discussion about the use of causal inference in economics. Whether it is feasible and whether there are benefits of working with a directed acyclic graph (DAG) and a non-parametric structural equation framework, is one aspect of the debate.

Regression and Causality

The causal effect of an intervention (treatment/exposure) on an outcome can be estimated by: i) specifying knowledge about the data-generating process; ii) assessing under what assumptions a target quantity, such as for example a causal odds ratio, can be identified given the specified knowledge (and given the measured data); and then, iii) using appropriate statistical estimation techniques to estimate the desired parameter of interest.

When and when not to use optimal model averaging

Using Longitudinal Targeted Maximum Likelihood Estimation in Complex Settings with Dynamic Interventions