Gregor Gößler

h-index19
2papers
1,203citations

2 Papers

10.3STOct 23, 2023
Identifiability of total effects from abstractions of time series causal graphs

Charles K. Assaad, Emilie Devijver, Eric Gaussier et al.

We study the problem of identifiability of the total effect of an intervention from observational time series in the situation, common in practice, where one only has access to abstractions of the true causal graph. We consider here two abstractions: the extended summary causal graph, which conflates all lagged causal relations but distinguishes between lagged and instantaneous relations, and the summary causal graph which does not give any indication about the lag between causal relations. We show that the total effect is always identifiable in extended summary causal graphs and provide sufficient conditions for identifiability in summary causal graphs. We furthermore provide adjustment sets allowing to estimate the total effect whenever it is identifiable.

3.3LOOct 10, 2017
Counterfactual Causality from First Principles?

Gregor Gössler, Oleg Sokolsky, Jean-Bernard Stefani

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.