Add to ORKGIn this paper we explore two quantitative approaches to the modelling of counterfactual reasoning – a linear and a noisy-OR model – based on in-formation contained in conceptual dependency networks. Empirical data is acquired in a study and the fit of the models compared to it. We con-clude by considering the appropriateness of non-parametric approaches to counterfactual reasoning, and examining the prospects for other para-metric approaches in the future.
In this paper I defend the view that counterfactual thinking depends on our causal representation of...
In this paper we present a model for argumentative causal and counterfactual reasoning in a logical ...
I develop an account of counterfactual conditionals using “causal models”, and argue that this accou...
In this paper we explore two quantitative approaches to the modelling of counterfactual reasoning – ...
Abstract: Recent work on the interpretation of counterfactual conditionals has paid much attention t...
Rethinking and introspection are important elements of human intelligence. To mimic these capabiliti...
<p>A successful theory of causal reasoning should be able to account for inferences about counterfac...
This thesis represents a contribution to the study of causal and counterfactual reasoning. In six ex...
Counterfactual frameworks have grown popular in machine learning for both explaining algorithmic dec...
In the artificial intelligence literature a promising approach to counterfactual reasoning is to int...
When people want to identify the causes of an event, assign credit or blame, or learn from their mis...
This paper contributes to the debate on the virtues and vices of counterfactuals as a basis for caus...
Causal models show promise as a foundation for the semantics of counterfactual sentences. However, c...
Counterfactual theories of causation of the sort presented in Mackie, 1974, and Lewis, 1973 are a fa...
We suggest a model that describes how counterfactuals are con-structed and justified. The model can ...
In this paper I defend the view that counterfactual thinking depends on our causal representation of...
In this paper we present a model for argumentative causal and counterfactual reasoning in a logical ...
I develop an account of counterfactual conditionals using “causal models”, and argue that this accou...
In this paper we explore two quantitative approaches to the modelling of counterfactual reasoning – ...
Abstract: Recent work on the interpretation of counterfactual conditionals has paid much attention t...
Rethinking and introspection are important elements of human intelligence. To mimic these capabiliti...
<p>A successful theory of causal reasoning should be able to account for inferences about counterfac...
This thesis represents a contribution to the study of causal and counterfactual reasoning. In six ex...
Counterfactual frameworks have grown popular in machine learning for both explaining algorithmic dec...
In the artificial intelligence literature a promising approach to counterfactual reasoning is to int...
When people want to identify the causes of an event, assign credit or blame, or learn from their mis...
This paper contributes to the debate on the virtues and vices of counterfactuals as a basis for caus...
Causal models show promise as a foundation for the semantics of counterfactual sentences. However, c...
Counterfactual theories of causation of the sort presented in Mackie, 1974, and Lewis, 1973 are a fa...
We suggest a model that describes how counterfactuals are con-structed and justified. The model can ...
In this paper I defend the view that counterfactual thinking depends on our causal representation of...
In this paper we present a model for argumentative causal and counterfactual reasoning in a logical ...
I develop an account of counterfactual conditionals using “causal models”, and argue that this accou...