Document type
Bachelor thesisPublication date
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/182557
Algorithmic causal effect identification
Journal Title
Authors
Director/Tutor
Journal ISSN
Volume Title
Related resource
Abstract
[en] Our evolution as a species made a huge step forward when we understood the relationships between causes and effects. These associations may be trivial for some events, but they are not in complex scenarios. To rigorously prove that some occurrences are caused by others, causal theory and causal inference were formalized, introducing the do-operator and its associated rules. The main goal of this project is to understand and implement in Python some algorithms to compute conditional and non-conditional causal queries from observational data. To this end, we first present some basic background knowledge on probability and graph theory, before introducing important results on causal theory, used in the construction of the algorithms. We then thoroughly study the identification algorithms presented by Shpitser and Pearl in 2006 [SP 2006a, SP 2006b], explaining our implementation in Python alongside. The main identification algorithm can be seen as a repeated application of the rules of do-calculus, and it eventually either returns an expression for the causal query from experimental probabilities or fails to identify the causal effect, in which case the effect is nonidentifiable. We introduce our newly developed Python library and give some usage examples towards the end of the dissertation.
Description
Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2021, Director: Jordi Vitrià i Marca i Álvaro Parafita Martínez
Subject (English)
Citation
Citation
PEDEMONTE BERNAT, Martí. Algorithmic causal effect identification. [consulted: 15 of June of 2026]. Available at: https://hdl.handle.net/2445/182557