AIMC Topic: Causality
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Harnessing causal forests for epidemiologic research: key considerations.
American journal of epidemiology
Jun 3, 2024
Assessing heterogeneous treatment effects (HTEs) is an essential task in epidemiology. The recent integration of machine learning into causal inference has provided a new, flexible tool for evaluating complex HTEs: causal forest. In a recent paper, J...
Practical Guide to Honest Causal Forests for Identifying Heterogeneous Treatment Effects.
American journal of epidemiology
Jul 7, 2023
"Heterogeneous treatment effects" is a term which refers to conditional average treatment effects (i.e., CATEs) that vary across population subgroups. Epidemiologists are often interested in estimating such effects because they can help detect popula...
The Causal Plausibility Decision in Healthcare.
Studies in health technology and informatics
Nov 3, 2022
The explosion of interest in exploiting machine learning techniques in healthcare has brought the issue of inferring causation from observational data to centre stage. In our work in supporting the health decisions of the individual person/patient-as...
Causes of Outcome Learning: a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome.
International journal of epidemiology
Oct 13, 2022
Nearly all diseases are caused by different combinations of exposures. Yet, most epidemiological studies focus on estimating the effect of a single exposure on a health outcome. We present the Causes of Outcome Learning approach (CoOL), which seeks t...
The Future of Causal Inference.
American journal of epidemiology
Sep 28, 2022
The past several decades have seen exponential growth in causal inference approaches and their applications. In this commentary, we provide our top-10 list of emerging and exciting areas of research in causal inference. These include methods for high...
Analysis of Causal Relationships in Integrated Ontologies of Diseases, Phenotypes, and Radiological Diagnosis.
Studies in health technology and informatics
Jun 6, 2022
Biomedical ontologies encode knowledge in a form that makes it computable. The current study used the integration of three large biomedical ontologies-the Disease Ontology (DO), Human Phenotype Ontology (HPO), and Radiology Gamuts Ontology (RGO)-to e...
AIPW: An R Package for Augmented Inverse Probability-Weighted Estimation of Average Causal Effects.
American journal of epidemiology
Dec 1, 2021
An increasing number of recent studies have suggested that doubly robust estimators with cross-fitting should be used when estimating causal effects with machine learning methods. However, not all existing programs that implement doubly robust estima...
Regularized Bayesian transfer learning for population-level etiological distributions.
Biostatistics (Oxford, England)
Oct 13, 2021
Computer-coded verbal autopsy (CCVA) algorithms predict cause of death from high-dimensional family questionnaire data (verbal autopsy) of a deceased individual, which are then aggregated to generate national and regional estimates of cause-specific ...
Invited Commentary: Machine Learning in Causal Inference-How Do I Love Thee? Let Me Count the Ways.
American journal of epidemiology
Aug 1, 2021
In this issue of the Journal, Mooney et al. (Am J Epidemiol. 2021;190(8):1476-1482) discuss machine learning as a tool for causal research in the style of Internet headlines. Here we comment by adapting famous literary quotations, including the one i...
Thirteen Questions About Using Machine Learning in Causal Research (You Won't Believe the Answer to Number 10!).
American journal of epidemiology
Aug 1, 2021
Machine learning is gaining prominence in the health sciences, where much of its use has focused on data-driven prediction. However, machine learning can also be embedded within causal analyses, potentially reducing biases arising from model misspeci...
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