Causal machine learning for healthcare and precision medicine
Causal machine learning (CML) has experienced increasing popularity in healthcare.
Beyond the inherent capabilities of adding domain knowledge into learning systems, CML …
Beyond the inherent capabilities of adding domain knowledge into learning systems, CML …
Artificial intelligence for quantitative modeling in drug discovery and development: An innovation and quality consortium perspective on use cases and best practices
Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered
in a new era of possibilities across various scientific domains. One area where these …
in a new era of possibilities across various scientific domains. One area where these …
Nonparametric estimation of heterogeneous treatment effects: From theory to learning algorithms
The need to evaluate treatment effectiveness is ubiquitous in most of empirical science, and
interest in flexibly investigating effect heterogeneity is growing rapidly. To do so, a multitude …
interest in flexibly investigating effect heterogeneity is growing rapidly. To do so, a multitude …
Explainable AI for glaucoma prediction analysis to understand risk factors in treatment planning
Glaucoma causes irreversible blindness. In 2020, about 80 million people worldwide had
glaucoma. Existing machine learning (ML) models are limited to glaucoma prediction, where …
glaucoma. Existing machine learning (ML) models are limited to glaucoma prediction, where …
Diffused responsibility: attributions of responsibility in the use of AI-driven clinical decision support systems
Good decision-making is a complex endeavor, and particularly so in a health context. The
possibilities for day-to-day clinical practice opened up by AI-driven clinical decision support …
possibilities for day-to-day clinical practice opened up by AI-driven clinical decision support …
Transfer learning on heterogeneous feature spaces for treatment effects estimation
Consider the problem of improving the estimation of conditional average treatment effects
(CATE) for a target domain of interest by leveraging related information from a source …
(CATE) for a target domain of interest by leveraging related information from a source …
Continuous-time modeling of counterfactual outcomes using neural controlled differential equations
Estimating counterfactual outcomes over time has the potential to unlock personalized
healthcare by assisting decision-makers to answer''what-iF''questions. Existing causal …
healthcare by assisting decision-makers to answer''what-iF''questions. Existing causal …
Estimating the effects of continuous-valued interventions using generative adversarial networks
While much attention has been given to the problem of estimating the effect of discrete
interventions from observational data, relatively little work has been done in the setting of …
interventions from observational data, relatively little work has been done in the setting of …
Really doing great at estimating CATE? a critical look at ML benchmarking practices in treatment effect estimation
A Curth, D Svensson, J Weatherall… - Thirty-fifth conference …, 2021 - openreview.net
The machine learning (ML) toolbox for estimation of heterogeneous treatment effects from
observational data is expanding rapidly, yet many of its algorithms have been evaluated …
observational data is expanding rapidly, yet many of its algorithms have been evaluated …
In search of insights, not magic bullets: Towards demystification of the model selection dilemma in heterogeneous treatment effect estimation
Personalized treatment effect estimates are often of interest in high-stakes applications–
thus, before deploying a model estimating such effects in practice, one needs to be sure that …
thus, before deploying a model estimating such effects in practice, one needs to be sure that …