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Causal Inference for Continuous Multiple Time Point Interventions

Recoverability of Causal Effects under Presence of Missing Data: a Longitudinal Case Study

Regression trees for nonparametric diagnostics of sequential positivity violations in longitudinal causal inference

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.