A survey on causal inference
Causal inference is a critical research topic across many domains, such as statistics,
computer science, education, public policy, and economics, for decades. Nowadays …
computer science, education, public policy, and economics, for decades. Nowadays …
From real‐world patient data to individualized treatment effects using machine learning: current and future methods to address underlying challenges
Clinical decision making needs to be supported by evidence that treatments are beneficial to
individual patients. Although randomized control trials (RCTs) are the gold standard for …
individual patients. Although randomized control trials (RCTs) are the gold standard for …
Time series deconfounder: Estimating treatment effects over time in the presence of hidden confounders
The estimation of treatment effects is a pervasive problem in medicine. Existing methods for
estimating treatment effects from longitudinal observational data assume that there are no …
estimating treatment effects from longitudinal observational data assume that there are no …
Deep proxy causal learning and its application to confounded bandit policy evaluation
Proxy causal learning (PCL) is a method for estimating the causal effect of treatments on
outcomes in the presence of unobserved confounding, using proxies (structured side …
outcomes in the presence of unobserved confounding, using proxies (structured side …
Reconsidering generative objectives for counterfactual reasoning
There has been recent interest in exploring generative goals for counterfactual reasoning,
such as individualized treatment effect (ITE) estimation. However, existing solutions often fail …
such as individualized treatment effect (ITE) estimation. However, existing solutions often fail …
[HTML][HTML] Deep representation learning for individualized treatment effect estimation using electronic health records
Utilizing clinical observational data to estimate individualized treatment effects (ITE) is a
challenging task, as confounding inevitably exists in clinical data. Most of the existing …
challenging task, as confounding inevitably exists in clinical data. Most of the existing …
Treatment effect prediction with adversarial deep learning using electronic health records
Background Treatment effect prediction (TEP) plays an important role in disease
management by ensuring that the expected clinical outcomes are obtained after performing …
management by ensuring that the expected clinical outcomes are obtained after performing …
[HTML][HTML] StudyU: a platform for designing and conducting innovative digital N-of-1 trials
S Konigorski, S Wernicke, T Slosarek… - Journal of Medical …, 2022 - jmir.org
N-of-1 trials are the gold standard study design to evaluate individual treatment effects and
derive personalized treatment strategies. Digital tools have the potential to initiate a new era …
derive personalized treatment strategies. Digital tools have the potential to initiate a new era …
Deep causal learning for robotic intelligence
Y Li - Frontiers in Neurorobotics, 2023 - frontiersin.org
This invited Review discusses causal learning in the context of robotic intelligence. The
Review introduces the psychological findings on causal learning in human cognition, as well …
Review introduces the psychological findings on causal learning in human cognition, as well …
Automated interpretable discovery of heterogeneous treatment effectiveness: A COVID-19 case study
BJ Lengerich, ME Nunnally… - Journal of biomedical …, 2022 - Elsevier
Testing multiple treatments for heterogeneous (varying) effectiveness with respect to many
underlying risk factors requires many pairwise tests; we would like to instead automatically …
underlying risk factors requires many pairwise tests; we would like to instead automatically …