In this paper, we consider score-based structure learning for the study of dynamical systems.
Counterfactual estimation using synthetic controls is one of the most successful recent methodological developments in causal inference.
Part of the challenge of learning robust models lies in the influence of unobserved confounders that void many of the invariances and principles of minimum error presently used for this problem.
The choice of making an intervention depends on its potential benefit or harm in comparison to alternatives.
Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals.
Analyzing electronic health records (EHR) poses significant challenges because often few samples are available describing a patient's health and, when available, their information content is highly diverse.
The co-occurrence of multiple diseases among the general population is an important problem as those patients have more risk of complications and represent a large share of health care expenditure.