Recent Publications

Here, you can find my most recent publications - including links to the pdf, code and other relevant materials. Further below, in the featured research section, I give some background on a few interesting papers.

A full publication list can be found here.
M. Schomaker, H. McIlleron, P. Denti, I. Dìaz (2024). Causal Inference for Continuous Multiple Time Point Interventions. Statistics in Medicine, 43(28): pp.5380-5400.

Preprint PDF Code Project Slides

M. Schomaker, P. Denti, A. Bienczak, D. Burger, I. Diaz, D. Gibb, S. Walker, H. McIlleron (2024). Determining Targets for Antiretroviral Drug Concentrations: a Causal Framework Illustrated with Pediatric Efavirenz Data from the CHAPAS-3 Trial. Pharmacoepidemiology and Drug Safety, 33:e70051.

PDF Project

A. Holovchak, H. McIlleron, P. Denti, M. Schomaker (2024). Recoverability of Causal Effects under Presence of Missing Data: a Longitudinal Case Study. Biostatistics, in press.

Preprint PDF Code Poster

A. Chatton, M. Schomaker, M. Luque-Fernandez, R. Platt, M. Schnitzer (2024). Regression trees for nonparametric diagnostics of sequential positivity violations in longitudinal causal inference. arXiv e-prints, https://arxiv.org/abs/2412.10245.

Preprint Code

See all publications

Featured Research

In this featured research section I give some background on a few interesting papers.

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.
Preprint PDF Code Dataset Project Slides Column Column (2)

The Effect of Electrical Load Shedding on Pediatric Hospital Admissions in South Africa

The Republic of South Africa (SA) faced repeated episodes of temporary power shutdowns in 2014/2015, but also in the years thereafter. Based on my co-author’s experience at the burns unit at Red Cross children’s hospital, we had the hypothesis that this may have caused an increase in pediatric hospital admissions.
PDF Slides Video Media

Bootstrap inference when using multiple imputation

For many analyses, it is common to use both bootstrapping and multiple imputation (MI): MI to address missing data and bootstrapping to obtain standard errors. For example, when using the g-formula in causal inference, bootstrapping is required to obtain standard errors; however, the data may be multiply imputed due to missing (baseline) data in the population of interest.
Preprint PDF Code Project