Algorithmic Causal Effect Identification with causaleffect

TL;DR

This paper reviews and implements algorithms for causal effect identification using the $do$-calculus in Python, enabling the computation of causal queries from observational data and providing a new library for practical use.

Contribution

It introduces a Python library implementing Shpitser and Pearl's causal identification algorithms, facilitating causal inference from observational data.

Findings

Successfully implemented causal identification algorithms in Python

Demonstrated the library with practical usage examples

Showed the algorithms can identify or fail to identify causal effects

Abstract

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 report is to review 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, 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 non-identifiable. We introduce our newly developed Python library and give some usage examples.