• Corpus ID: 214774784

Counterfactuals and Dependencies on Causal Teams: Expressive Power and Deduction Systems

@inproceedings{Barbero2020CounterfactualsAD,
  title={Counterfactuals and Dependencies on Causal Teams: Expressive Power and Deduction Systems},
  author={Fausto Barbero and Fan Yang},
  booktitle={AiML},
  year={2020}
}
We analyze the causal-observational languages that were introduced in Barbero and Sandu (2018), which allow discussing interventionist counterfactuals and functional dependencies in a unified framework. In particular, we systematically investigate the expressive power of these languages in causal team semantics, and we provide complete natural deduction calculi for each language. As an intermediate step towards the completeness, we axiomatize the languages over a generalized version of causal… 
View PDF on arXiv
View With Semantic Reader
4 Citations
Background Citations
3

4 Citations

Citation Type

Citation Type

Characterizing counterfactuals and dependencies over causal and generalized causal teams
TLDR
A generalized semantics is introduced which allows representing uncertainty about the causal laws, and the expressive power and proof theory of the causal-observational languages over this enriched semantics are analyzed.
Thinking About Causation: A Causal Language with Epistemic Operators
This paper proposes a formal framework for modeling the interaction of causal and (qualitative) epistemic reasoning. To this purpose, we extend the notion of a causal model with a representation of…
Observing Interventions: A logic for thinking about experiments
TLDR
This paper investigates formal frameworks for modeling the interaction of causal and (qualitative) epistemic reasoning and provides a sound and complete axiomatization of the proposed logics.
Dynamic Logic. New Trends and Applications: Third International Workshop, DaLí 2020, Prague, Czech Republic, October 9–10, 2020, Revised Selected Papers
TLDR
This work identifies a graph structure for events based on their relative dependence of occurence and introduces a generic product update based on the concepts of event model and product update.

References

SHOWING 1-10 OF 21 REFERENCES
Team Semantics for Interventionist Counterfactuals: Observations vs. Interventions
TLDR
It is shown that the “causal teams” introduced in the present paper can be used for modelling some classical counterfactual scenarios which are not captured by the usual causal models.
Team semantics for interventionist counterfactuals and causal dependence
We introduce a generalization of team semantics which provides a framework for manipulationist theories of causation based on structural equation models, such as Woodward's and Pearl's; our causal…
An Axiomatic Characterization of Causal Counterfactuals
This paper studies the causal interpretation of counterfactual sentences using a modifiable structural equation model. It is shown that two properties of counterfactuals, namely, composition and…
A Lewisian Logic of Causal Counterfactuals
TLDR
This paper characterize the class of causal models that are Lewisian in the sense that they validate the principles in Lewis’s well-known logic of counterfactuals and develops a system sound and complete with respect to this class.
Compositional Semantics for a Language of Imperfect Information
  • W. Hodges
  • Philosophy, Computer Science
    Log. J. IGPL
  • 1997
TLDR
A compositional semantics for this logic is described, which gives a compositional meaning to formulas of the ‘information-friendly’ language of Hintikka and Sandu, and proves that two formulas have the same interpretation in all structures.
The Similarity of Causal Structure
TLDR
Since there are multiple principled senses in which graphs can be similar, this work introduces multiple similarity metrics and multiple ways to prioritize these metrics.
Axiomatizing Causal Reasoning
TLDR
It is shown that to reason about causality in the most general third class, it must extend the language used by Galles and Pearl.
Propositional team logics
Propositional logics of dependence
Causality: Models, Reasoning and Inference
1. Introduction to probabilities, graphs, and causal models 2. A theory of inferred causation 3. Causal diagrams and the identification of causal effects 4. Actions, plans, and direct effects 5.…
...
...