Identifying Causal Effects with the R Package causaleffect

TL;DR

The paper introduces the R package causaleffect, which implements an algorithm based on do-calculus to identify causal effects from observational data within causal models, aiding researchers in causal inference.

Contribution

It provides the first practical implementation of an algorithm for identifying causal effects in models with unobserved variables using do-calculus rules.

Findings

Successfully derives causal effects in example models

Identifies non-identifiable effects in complex models

Facilitates causal inference with an accessible R package

Abstract

Do-calculus is concerned with estimating the interventional distribution of an action from the observed joint probability distribution of the variables in a given causal structure. All identifiable causal effects can be derived using the rules of do-calculus, but the rules themselves do not give any direct indication whether the effect in question is identifiable or not. Shpitser and Pearl constructed an algorithm for identifying joint interventional distributions in causal models, which contain unobserved variables and induce directed acyclic graphs. This algorithm can be seen as a repeated application of the rules of do-calculus and known properties of probabilities, and it ultimately either derives an expression for the causal distribution, or fails to identify the effect, in which case the effect is non-identifiable. In this paper, the R package causaleffect is presented, which provides an implementation of this algorithm. Functionality of causaleffect is also demonstrated through examples.