Anne H. Petersen

• PhD
• Assistant Professor at University of Copenhagen

Temporal background information can improve causal discovery algorithms by orienting edges and identifying relevant adjustment sets. We develop the Temporal Greedy Equivalence Search (TGES) algorithm and terminology essential for score-based causal discovery with tiered background knowledge. TGES learns a restricted Markov equivalence class of dire...

This paper examines the potential benefits of a deeper integration between system dynamics and causal inference as applied in epidemiology. We offer four suggestions for bridging these fields: two for what system dynamics can offer and two for what system dynamics stands to gain. First, we discuss the use of system dynamics to develop simulation mo...

New proposals for causal discovery algorithms are typically evaluated using simulations and a few select real data examples with known data generating mechanisms. However, there does not exist a general guideline for how such evaluation studies should be designed, and therefore, comparing results across different studies can be difficult. In this a...

Life course epidemiology relies on specifying complex (causal) models that describe how variables interplay over time. Traditionally, such models have been constructed by perusing existing theory and previous studies. By comparing data-driven and theory-driven models, we investigate whether data-driven causal discovery algorithms can help this proc...

Causal inference can estimate causal effects, but unless data are collected experimentally, statistical analyses must rely on pre-specified causal models. Causal discovery algorithms are empirical methods for constructing such causal models from data. Several asymptotically correct discovery methods already exist, but they generally struggle on sma...

Causal inference can estimate causal effects, but unless data are collected experimentally, statistical analyses must rely on pre-specified causal models. Causal discovery algorithms are empirical methods for constructing such causal models from data. Several asymptotically correct methods already exist, but they generally struggle on smaller sampl...

The recent paper by Sariaslan et al.¹ on the causal impact of childhood family income has gained attention in the field of social medicine. And rightfully so, as it is a comprehensive, well-conducted and important piece of epidemiological work. It is also one of few instances in which a precise null-finding may have important political implications...

Life course epidemiology is useful for describing and analyzing complex etiological mechanisms for disease development, but existing statistical methods are essentially confirmatory, as they rely on a priori model specification. This limits the scope of causal inquiries that can be made, since these methods are mostly suited to examine well-known h...

Datasets are sometimes divided into distinct subsets, e.g. due to multi-center sampling, or to variations in instruments, questionnaire item ordering or mode of administration, and the data analyst then needs to assess whether a joint analysis is meaningful. The Principal Component Analysis-based Data Structure Comparisons (PCADSC) tools are three...

Background The gut microbiota of children delivered by cesarean section differs from that of children delivered vaginally. In light of the gut-brain axis hypothesis, cesarean section may influence risk of affective disorders. Methods Population based prospective cohort study included Danish children born 1982 through 2001, with follow-up until 201...

Sibling comparison designs have long been used to assess causal effects of exposures for which randomized studies are impossible and measurement of all relevant confounding is unobtainable. The idea is to utilize the fact that siblings often share a lot of unobserved variables. Therefore, it is proposed that in certain cases, comparing siblings is...

Aim: To examine whether adding the Community Reinforcement Approach for Seniors (CRA-S) to Motivational Enhancement Therapy (MET) increases the probability of treatment success in people ≥ 60 years old with alcohol use disorder (AUD). Design: A single blind multi-centre multinational randomized (1:1) controlled trial. Setting: Outpatient setti...

Data cleaning and validation are important steps in any data analysis, as the validity of the conclusions from the analysis hinges on the quality of the input data. Mistakes in the data can arise for any number of reasons, including erroneous codings, malfunctioning measurement equipment, and inconsistent data generation manuals. Ideally, a human i...

Background: Hypotheses concerning adverse effects of changes in microbiota have received much recent attention, but unobserved confounding makes them difficult to test. We investigated whether surrogate markers for potential adverse microbiota changes in infancy affected autism risk, addressing unobserved confounding using a sibling study design....

Background Increasing attention deficit hyperactivity disorder (ADHD) incidence has been proposed to be caused by factors influencing microbiota in early life. We investigated the potential causality between ADHD and two surrogate markers for changes in children's microbiota: birth delivery mode and early childhood antibiotic use. Method This popu...

Background: In patients with intestinal failure who are receiving home parenteral support (HPS), catheter-related bloodstream infections (CRBSIs) inflict health impairment and high costs.Objective: This study investigates the efficacy and safety of the antimicrobial catheter lock solution, taurolidine-citrate-heparin, compared with heparin 100 IE/m...

Survival prognosis is challenging, and accurate prediction of individual survival times is often very difficult. Better statistical methodology and more data can help improve the prognostic models, but it is important that methods and data usages are evaluated properly. The Prostate Cancer DREAM Challenge offered a framework for training and blinde...