Search Results for author:
Found 10 papers, 2 papers with code
Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods
This survey paper covers the breadth and depth of time-series and spatiotemporal causality methods, and their applications in Earth Science.
KGS: Causal Discovery Using Knowledge-guided Greedy Equivalence Search
Prior causal information such as the presence or absence of a causal edge can be leveraged to guide the discovery process towards a more restricted and accurate search space.
A Survey on Causal Discovery Methods for I.I.D. and Time Series Data
The ability to understand causality from data is one of the major milestones of human-level intelligence.
eCDANs: Efficient Temporal Causal Discovery from Autocorrelated and Non-stationary Data (Student Abstract)
Conventional temporal causal discovery (CD) methods suffer from high dimensionality, fail to identify lagged causal relationships, and often ignore dynamics in relations.
RFC-Net: Learning High Resolution Global Features for Medical Image Segmentation on a Computational Budget
Learning High-Resolution representations is essential for semantic segmentation.
CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Data
Our proposed method addresses several limitations of existing causal discovery methods for autocorrelated and non-stationary time series data, such as high dimensionality, the inability to identify lagged causal relationships, and overlooking changing modules.
CKH: Causal Knowledge Hierarchy for Estimating Structural Causal Models from Data and Priors
Using CKH, we present a methodological framework for encoding causal priors from various information sources and combining them to derive an SCM.
Causal Discovery on the Effect of Antipsychotic Drugs on Delirium Patients in the ICU using Large EHR Dataset
To explore safety outcomes associated with APD, we aim to build a causal model for delirium in the ICU using large observational data sets connecting various covariates correlated with delirium.
Multispectral Object Detection with Deep Learning
Moving outside the visible spectrum range, such as the thermal spectrum or the near-infrared (NIR) images, is much more beneficial in low visibility conditions, NIR images are very helpful for understanding the object's material quality.
Structural Causal Model with Expert Augmented Knowledge to Estimate the Effect of Oxygen Therapy on Mortality in the ICU
Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identification of causal effects from observational data in the cases where the causal graph is identifiable, i. e., the data generating mechanism can be recovered from the joint distribution.
