The previous two years, we hosted causal inference workshops at the EDM international conferences with great success. Over 30 people attended each of the workshops, and both workshops featured stimulating talks on recently completed projects, works in progress, and open questions. Due to the success of the first workshop, we would like to organize a third workshop on causal inference. Given the recent influx of users to remote learning platforms due to COVID-19, and the interest in investigating the effect of remote learning on students, the need to analyze observational data has grown. A workshop on causal inference can help facilitate discussion and educate new researchers on ways of analyzing the recent breadth of data available. In gen...
Researchers tasked with understanding the effects of educational technology innovations face the cha...
Inferring a system’s underlying mechanisms is a primary goal in many areas of science. For instance,...
We explore relationships between machine learning (ML) and causal inference. We focus on improvement...
Machine learning has traditionally been focused on prediction. Given observations that have been gen...
New statistical methods allow discovery of causal models from observational data in some circumstanc...
Causality is a complex concept, which roots its developments across several fields, such as statisti...
A concise and self-contained introduction to causal inference, increasingly important in data scienc...
To identify the ways teachers and educational systems can improve learning, researchers need to make...
This thesis examines causal discovery within datasets, in particular observational datasets where no...
ii New statistical methods allow discovery of causal models from observational data in some circumst...
The two fields of machine learning and graphical causality arose and are developed separately. Howev...
The Working Paper gives an overview about the topic of causal inference,covered in the Institute on ...
Relationships between causes and their effects take a central role in various scientific and societa...
Contains fulltext : 132650.pdf (publisher's version ) (Closed access)60 p
The field of causal learning has grown in the past decade, establishing itself as a major focus in a...
Researchers tasked with understanding the effects of educational technology innovations face the cha...
Inferring a system’s underlying mechanisms is a primary goal in many areas of science. For instance,...
We explore relationships between machine learning (ML) and causal inference. We focus on improvement...
Machine learning has traditionally been focused on prediction. Given observations that have been gen...
New statistical methods allow discovery of causal models from observational data in some circumstanc...
Causality is a complex concept, which roots its developments across several fields, such as statisti...
A concise and self-contained introduction to causal inference, increasingly important in data scienc...
To identify the ways teachers and educational systems can improve learning, researchers need to make...
This thesis examines causal discovery within datasets, in particular observational datasets where no...
ii New statistical methods allow discovery of causal models from observational data in some circumst...
The two fields of machine learning and graphical causality arose and are developed separately. Howev...
The Working Paper gives an overview about the topic of causal inference,covered in the Institute on ...
Relationships between causes and their effects take a central role in various scientific and societa...
Contains fulltext : 132650.pdf (publisher's version ) (Closed access)60 p
The field of causal learning has grown in the past decade, establishing itself as a major focus in a...
Researchers tasked with understanding the effects of educational technology innovations face the cha...
Inferring a system’s underlying mechanisms is a primary goal in many areas of science. For instance,...
We explore relationships between machine learning (ML) and causal inference. We focus on improvement...