[spa] En el presente trabajo de fin de máster, se busca realizar una introducción a la inferencia causal, tanto desde un enfoque teórico como práctico, poniendo principal énfasis en los avances que se han producido en dicha disciplina a raíz de la introducción del machine learning. Para ello, se introducirá inicialmente el marco teórico y técnico convencional de la inferencia causal, suministrando análisis y ejemplos prácticos, para continuar con la exposición de las nuevas técnicas y ventajas suministradas con la inclusión del machine learning.[eng] The main objective of the present master’s thesis consists in the introduction of the causal inference discipline from both theorical and practical perspective, paying special attention...
This article reviews recent advances in causal inference relevant to sociology. We focus on a select...
Establishing causality has been a problem throughout history of philosophy of science. This paper di...
Causal inference -- the process of drawing a conclusion about the impact of an exposure on an outcom...
[EN] Causality is a fundamental part of reasoning to model the physics of an application domain, to ...
The goal of this tutorial is twofold: to provide a description of some basic causal inference proble...
A concise and self-contained introduction to causal inference, increasingly important in data scienc...
“Causality” is a complex concept that is based on roots in almost all subject areas and aims to answ...
TESIS PARA OPTAR AL GRADO DE MAGISTER EN ANÁLISIS ECONÓMICObeen for a long time one of the main focu...
Causality is a complex concept, which roots its developments across several fields, such as statisti...
We explore relationships between machine learning (ML) and causal inference. We focus on improvement...
El beneficio principal de contar con una representación de la potencia causal (Cheng, 1997) es que é...
The two fields of machine learning and graphical causality arose and are developed separately. Howev...
This paper provides a link between causal inference and machine learning techniques - specifically, ...
Experiments have always been the way to study what the effect is of interventions. Causal inference ...
The main task in causal inference is the prediction of the outcome of an in-tervention. For example,...
This article reviews recent advances in causal inference relevant to sociology. We focus on a select...
Establishing causality has been a problem throughout history of philosophy of science. This paper di...
Causal inference -- the process of drawing a conclusion about the impact of an exposure on an outcom...
[EN] Causality is a fundamental part of reasoning to model the physics of an application domain, to ...
The goal of this tutorial is twofold: to provide a description of some basic causal inference proble...
A concise and self-contained introduction to causal inference, increasingly important in data scienc...
“Causality” is a complex concept that is based on roots in almost all subject areas and aims to answ...
TESIS PARA OPTAR AL GRADO DE MAGISTER EN ANÁLISIS ECONÓMICObeen for a long time one of the main focu...
Causality is a complex concept, which roots its developments across several fields, such as statisti...
We explore relationships between machine learning (ML) and causal inference. We focus on improvement...
El beneficio principal de contar con una representación de la potencia causal (Cheng, 1997) es que é...
The two fields of machine learning and graphical causality arose and are developed separately. Howev...
This paper provides a link between causal inference and machine learning techniques - specifically, ...
Experiments have always been the way to study what the effect is of interventions. Causal inference ...
The main task in causal inference is the prediction of the outcome of an in-tervention. For example,...
This article reviews recent advances in causal inference relevant to sociology. We focus on a select...
Establishing causality has been a problem throughout history of philosophy of science. This paper di...
Causal inference -- the process of drawing a conclusion about the impact of an exposure on an outcom...