Search Results for author:
Found 3 papers, 1 papers with code
Causal Graph Discovery from Self and Mutually Exciting Time Series
We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series.
Granger Causal Chain Discovery for Sepsis-Associated Derangements via Continuous-Time Hawkes Processes
Modern health care systems are conducting continuous, automated surveillance of the electronic medical record (EMR) to identify adverse events with increasing frequency; however, many events such as sepsis do not have elucidated prodromes (i. e., event chains) that can be used to identify and intercept the adverse event early in its course.
Causal Graph Discovery from Self and Mutually Exciting Time Series
We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series.
