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Identifiability of total effects from abstractions of time series causal graphs
We study the problem of identifiability of the total effect of an intervention from observational time series only given an abstraction of the causal graph of the system.
Counterfactual Causality from First Principles?
In this position paper we discuss three main shortcomings of existing approaches to counterfactual causality from the computer science perspective, and sketch lines of work to try and overcome these issues: (1) causality definitions should be driven by a set of precisely specified requirements rather than specific examples; (2) causality frameworks should support system dynamics; (3) causality analysis should have a well-understood behavior in presence of abstraction.
Proceedings First Workshop on Causal Reasoning for Embedded and safety-critical Systems Technologies
The keynote by Hana Chockler (King's College) provided a broad perspective on the application of causal reasoning based on Halpern and Pearl's definitions of actual causality to a variety of application domains ranging from formal verification to legal reasoning.
