Emulate randomized clinical trials using heterogeneous treatment effect estimation for personalized treatments: Methodology review and benchmark
Big data and (deep) machine learning have been ambitious tools in digital medicine, but
these tools focus mainly on association. Intervention in medicine is about the causal effects …
these tools focus mainly on association. Intervention in medicine is about the causal effects …
The hardness of conditional independence testing and the generalised covariance measure
The hardness of conditional independence testing and the generalised covariance measure
Page 1 The Annals of Statistics 2020, Vol. 48, No. 3, 1514–1538 https://doi.org/10.1214/19-AOS1857 …
Page 1 The Annals of Statistics 2020, Vol. 48, No. 3, 1514–1538 https://doi.org/10.1214/19-AOS1857 …
Nonparametric causal effects based on incremental propensity score interventions
EH Kennedy - Journal of the American Statistical Association, 2019 - Taylor & Francis
Most work in causal inference considers deterministic interventions that set each unit's
treatment to some fixed value. However, under positivity violations these interventions can …
treatment to some fixed value. However, under positivity violations these interventions can …
[HTML][HTML] Employee benefits and company performance: Evidence from a high-dimensional machine learning model
By incorporating novel social media data, we analyze in detail how US companies offer
different employee benefits and how they are associated with several company performance …
different employee benefits and how they are associated with several company performance …
Kernel-based covariate functional balancing for observational studies
RKW Wong, KCG Chan - Biometrika, 2018 - academic.oup.com
Covariate balance is often advocated for objective causal inference since it mimics
randomization in observational data. Unlike methods that balance specific moments of …
randomization in observational data. Unlike methods that balance specific moments of …
Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmark
Developing new drugs for target diseases is a time-consuming and expensive task, drug
repurposing has become a popular topic in the drug development field. As much health …
repurposing has become a popular topic in the drug development field. As much health …
Learning weighted representations for generalization across designs
Predictive models that generalize well under distributional shift are often desirable and
sometimes crucial to building robust and reliable machine learning applications. We focus …
sometimes crucial to building robust and reliable machine learning applications. We focus …
Nonparametric efficient causal mediation with intermediate confounders
Interventional effects for mediation analysis were proposed as a solution to the lack of
identifiability of natural (in) direct effects in the presence of a mediator-outcome confounder …
identifiability of natural (in) direct effects in the presence of a mediator-outcome confounder …
Machine learning for sarcopenia prediction in the elderly using socioeconomic, infrastructure, and quality-of-life data
M Seok, W Kim, J Kim - Healthcare, 2023 - mdpi.com
Since the WHO's 2021 aging redefinition emphasizes “healthy aging” by focusing on the
elderly's ability to perform daily activities, sarcopenia, which is defined as the loss of skeletal …
elderly's ability to perform daily activities, sarcopenia, which is defined as the loss of skeletal …
[HTML][HTML] Learning end-to-end patient representations through self-supervised covariate balancing for causal treatment effect estimation
A causal effect can be defined as a comparison of outcomes that result from two or more
alternative actions, with only one of the action-outcome pairs actually being observed. In …
alternative actions, with only one of the action-outcome pairs actually being observed. In …