Causal inference is perhaps the most important form of reasoning in the sciences. A panoply of disciplines, ranging from epidemiology to biology, from econometrics to physics, make use of probability and statistics in order to infer causal relationships. However, the very foundations of causal inference are up in the air; it is by no means clear which methods of causal inference should be used, nor why they work when they do. This book brings philosophers and scientists together to tackle these important questions. The papers in this volume shed light on the relationship between causality and probability and the application of these concepts within the sciences. With its interdisciplinary perspective and its careful analysis, "Causality and...
Probability and Causality is a critical analysis of the problem of causality in indeterministic cont...
In this paper causality is seen from a pluralist point of view: About its physical reality one can n...
Experiments have always been the way to study what the effect is of interventions. Causal inference ...
There is a need for integrated thinking about causality, probability and mechanisms in scientific me...
NoCausal inference is perhaps the most important form of reasoning in the sciences. A panoply of dis...
A state of the art volume on statistical causality Causality: Statistical Perspectives and Applicati...
Providing a thorough treatment on statistical causality, this resource presents a broad collection o...
Providing a thorough treatment on statistical causality, this resource presents a broad collection o...
The anti-causal prophecies of last century have been disproved. Causality is neither a ‘relic of a b...
The concept of cause is of extraordinary importance for the sci- ences. Scientists want to know the ...
Establishing causality has been a problem throughout history of philosophy of science. This paper di...
Philosophers and statisticians have been debating on causality for a long time. However, these discu...
Aim: A statistical methodology based analysis of causal relationships and chains of causation in lif...
We argue that the health sciences make causal claims on the basis of evidence both of physical mecha...
This book compiles and presents new developments in statistical causal inference. The accompanying d...
Probability and Causality is a critical analysis of the problem of causality in indeterministic cont...
In this paper causality is seen from a pluralist point of view: About its physical reality one can n...
Experiments have always been the way to study what the effect is of interventions. Causal inference ...
There is a need for integrated thinking about causality, probability and mechanisms in scientific me...
NoCausal inference is perhaps the most important form of reasoning in the sciences. A panoply of dis...
A state of the art volume on statistical causality Causality: Statistical Perspectives and Applicati...
Providing a thorough treatment on statistical causality, this resource presents a broad collection o...
Providing a thorough treatment on statistical causality, this resource presents a broad collection o...
The anti-causal prophecies of last century have been disproved. Causality is neither a ‘relic of a b...
The concept of cause is of extraordinary importance for the sci- ences. Scientists want to know the ...
Establishing causality has been a problem throughout history of philosophy of science. This paper di...
Philosophers and statisticians have been debating on causality for a long time. However, these discu...
Aim: A statistical methodology based analysis of causal relationships and chains of causation in lif...
We argue that the health sciences make causal claims on the basis of evidence both of physical mecha...
This book compiles and presents new developments in statistical causal inference. The accompanying d...
Probability and Causality is a critical analysis of the problem of causality in indeterministic cont...
In this paper causality is seen from a pluralist point of view: About its physical reality one can n...
Experiments have always been the way to study what the effect is of interventions. Causal inference ...