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Causal Models with Constraints
The goal of this paper is to extend standard causal models to allow for constraints on settings of variables.
Backtracking Counterfactuals
In Pearl's structural causal model (SCM) framework this is made mathematically rigorous via interventions that modify the causal laws while the values of exogenous variables are shared.
A Causal Analysis of Harm
In this paper we formally define a qualitative notion of harm that uses causal models and is based on a well-known definition of actual causality (Halpern, 2016).
Quantifying Harm
In a companion paper (Beckers et al. 2022), we defined a qualitative notion of harm: either harm is caused, or it is not.
Causal Explanations and XAI
Although the definitions are motivated by a focus on XAI, the analysis of causal explanation and actual causation applies in general.
Causal Sufficiency and Actual Causation
One definition comes out as being superior to all others, and is therefore suggested as a new definition of actual causation.
Equivalent Causal Models
The aim of this paper is to offer the first systematic exploration and definition of equivalent causal models in the context where both models are not made up of the same variables.
The Counterfactual NESS Definition of Causation
In previous work with Joost Vennekens I proposed a definition of actual causation that is based on certain plausible principles, thereby allowing the debate on causation to shift away from its heavy focus on examples towards a more systematic analysis.
Approximate Causal Abstraction
Abstract descriptions can provide the basis for interventions on the system and explanation of observed phenomena at a level of granularity that is coarser than the most fundamental account of the system.
Abstracting Causal Models
We consider a sequence of successively more restrictive definitions of abstraction for causal models, starting with a notion introduced by Rubenstein et al. (2017) called exact transformation that applies to probabilistic causal models, moving to a notion of uniform transformation that applies to deterministic causal models and does not allow differences to be hidden by the "right" choice of distribution, and then to abstraction, where the interventions of interest are determined by the map from low-level states to high-level states, and strong abstraction, which takes more seriously all potential interventions in a model, not just the allowed interventions.
Combining Probabilistic, Causal, and Normative Reasoning in CP-logic
In recent years the search for a proper formal definition of actual causation -- i. e., the relation of cause-effect as it is instantiated in specific observations, rather than general causal relations -- has taken on impressive proportions.
Towards a General Framework for Actual Causation Using CP-logic
The search for a proper formal definition of actual causation has evolved into a controversial debate, that is pervaded with ambiguities and confusion.
