Introduction to computational causal inference using reproducible Stata, R, and Python code: a tutorial

MJ Smith, MA Mansournia, C Maringe… - Statistics in …, 2022 - Wiley Online Library
The main purpose of many medical studies is to estimate the effects of a treatment or
exposure on an outcome. However, it is not always possible to randomize the study …

[HTML][HTML] Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review

MJ Smith, RV Phillips, MA Luque-Fernandez… - Annals of …, 2023 - Elsevier
Purpose The targeted maximum likelihood estimation (TMLE) statistical data analysis
framework integrates machine learning, statistical theory, and statistical inference to provide …

Challenges in obtaining valid causal effect estimates with machine learning algorithms

AI Naimi, AE Mishler, EH Kennedy - American Journal of …, 2023 - academic.oup.com
Unlike parametric regression, machine learning (ML) methods do not generally require
precise knowledge of the true data-generating mechanisms. As such, numerous authors …

AIPW: An R Package for Augmented Inverse Probability–Weighted Estimation of Average Causal Effects

Y Zhong, EH Kennedy, LM Bodnar… - American Journal of …, 2021 - academic.oup.com
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 …

Analyses of child cardiometabolic phenotype following assisted reproductive technologies using a pragmatic trial emulation approach

JY Huang, S Cai, Z Huang, MT Tint, WL Yuan… - Nature …, 2021 - nature.com
Assisted reproductive technologies (ART) are increasingly used, however little is known
about the long-term health of ART-conceived offspring. Weak selection of comparison …

Bladder cancer radiation oncology of the future: prognostic modelling, radiomics, and treatment planning with artificial intelligence

NS Moore, A McWilliam, S Aneja - Seminars in Radiation Oncology, 2023 - Elsevier
Machine learning (ML) and artificial intelligence (AI) have demonstrated potential to improve
the care of radiation oncology patients. Here we review recent advances applicable to the …

Don't Let Your Analysis Go to Seed: On the Impact of Random Seed on Machine Learning-based Causal Inference

L Schader, W Song, R Kempker, D Benkeser - Epidemiology, 2024 - journals.lww.com
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Machine learning for improving high‐dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature

R Wyss, C Yanover, T El‐Hay, D Bennett… - … and drug safety, 2022 - Wiley Online Library
Purpose Supplementing investigator‐specified variables with large numbers of empirically
identified features that collectively serve as 'proxies' for unspecified or unmeasured factors …

[HTML][HTML] Is the association between fruits and vegetables and preeclampsia due to higher dietary vitamin C and carotenoid intakes?

LM Bodnar, SI Kirkpatrick, JM Roberts… - The American Journal of …, 2023 - Elsevier
Background Diets dense in fruits and vegetables are associated with a reduced risk of
preeclampsia, but pathways underlying this relationship are unclear. Dietary antioxidants …

Transportability without positivity: a synthesis of statistical and simulation modeling

PN Zivich, JK Edwards, ET Lofgren, SR Cole… - …, 2024 - journals.lww.com
Studies designed to estimate the effect of an action in a randomized or observational setting
often do not represent a random sample of the desired target population. Instead, estimates …