Temporal Causal Reasoning with (Non-Recursive) Structural Equation Models

Authors: Maksim Gladyshev, Natasha Alechina, Mehdi Dastani, Dragan Doder, Brian Logan

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Reproducibility Variable Result LLM Response
Research Type Theoretical In this paper we propose a new interpretation of SEMs when reasoning about Actual Causality, in which SEMs are viewed as mechanisms transforming the dynamics of exogenous variables into the dynamics of endogenous variables. This allows us to combine counterfactual causal reasoning with existing temporal logic formalisms, and to introduce a temporal logic, CPLTL, for causal reasoning about such structures. We show that the standard restriction to so-called recursive models (with no cycles in the dependency graph) is not necessary in our approach, allowing us to reason about mutually dependent processes and feedback loops. Finally, we introduce new notions of model equivalence for temporal causal models, and show that CPLTL has an efficient model-checking procedure.
Researcher Affiliation Academia 1 Utrecht University, The Netherlands 2 Open Universiteit, The Netherlands 3 University of Aberdeen, UK
Pseudocode Yes Algorithm 1: Computing C Y ( n) y of (M, u, v, Y ( n) y), type of u is (n,m)
Open Source Code No No explicit statement or link for open-source code for the methodology described in this paper was found.
Open Datasets No The paper illustrates its concepts with examples like 'Example 1 (Rocks)', 'Example 2 (Treatment)', and 'Example 3 (Deadline)' rather than using specific public datasets for empirical evaluation.
Dataset Splits No The paper does not describe experiments that use external datasets, and therefore no information about dataset splits is provided.
Hardware Specification No The paper focuses on theoretical contributions and does not describe experiments that would require specific hardware specifications.
Software Dependencies No The paper mentions using 'a PLTL path model-checker (Markey 2002)' as part of the theoretical model-checking procedure, but it does not specify any software dependencies with version numbers for empirical reproduction of experiments.
Experiment Setup No The paper describes a theoretical framework and does not include details on experimental setup, hyperparameters, or training configurations.