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 …
exposure on an outcome. However, it is not always possible to randomize the study …
Machine learning in policy evaluation: new tools for causal inference
N Kreif, K DiazOrdaz - arXiv preprint arXiv:1903.00402, 2019 - arxiv.org
While machine learning (ML) methods have received a lot of attention in recent years, these
methods are primarily for prediction. Empirical researchers conducting policy evaluations …
methods are primarily for prediction. Empirical researchers conducting policy evaluations …
A primer on inverse probability of treatment weighting and marginal structural models
F Thoemmes, AD Ong - Emerging Adulthood, 2016 - journals.sagepub.com
Emerging adulthood researchers are often interested in the effects of developmental tasks.
The majority of transitions that occur during the period of early/emerging adulthood are not …
The majority of transitions that occur during the period of early/emerging adulthood are not …
Context-specific transcription factor functions regulate epigenomic and transcriptional dynamics during cardiac reprogramming
Ectopic expression of combinations of transcription factors (TFs) can drive direct lineage
conversion, thereby reprogramming a somatic cell's identity. To determine the molecular …
conversion, thereby reprogramming a somatic cell's identity. To determine the molecular …
Using super learner prediction modeling to improve high-dimensional propensity score estimation
The high-dimensional propensity score is a semiautomated variable selection algorithm that
can supplement expert knowledge to improve confounding control in nonexperimental …
can supplement expert knowledge to improve confounding control in nonexperimental …
The association of ambient air pollution and traffic exposures with selected congenital anomalies in the San Joaquin Valley of California
Congenital anomalies are a leading cause of infant mortality and are important contributors
to subsequent morbidity. Studies suggest associations between environmental …
to subsequent morbidity. Studies suggest associations between environmental …
Implementing statistical methods for generalizing randomized trial findings to a target population
Randomized trials are considered the gold standard for assessing the causal effects of a
drug or intervention in a study population, and their results are often utilized in the …
drug or intervention in a study population, and their results are often utilized in the …
Semi-parametric estimation and inference for the mean outcome of the single time-point intervention in a causally connected population
O Sofrygin, MJ van der Laan - Journal of causal inference, 2017 - degruyter.com
We study the framework for semi-parametric estimation and statistical inference for the
sample average treatment-specific mean effects in observational settings where data are …
sample average treatment-specific mean effects in observational settings where data are …
[HTML][HTML] A doubly robust approach for impact evaluation of interventions for business process improvement based on event logs
Accurately measuring the causal effects of business process interventions is crucial for
effective process improvement and evidence-based decision-making. When randomized …
effective process improvement and evidence-based decision-making. When randomized …
Causes and consequences of child growth failure in low-and middle-income countries
Child growth failure is associated with a higher risk of illness and mortality, which
contributed to the United Nations Sustainable Development Goal 2.2 to end malnutrition by …
contributed to the United Nations Sustainable Development Goal 2.2 to end malnutrition by …