Algorithmic Causal Effect Identification with causaleffect

by   Martí Pedemonte, et al.

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.


page 1

page 2

page 3

page 4


Identifying Causal Effects with the R Package causaleffect

Do-calculus is concerned with estimating the interventional distribution...

MERLiN: Mixture Effect Recovery in Linear Networks

Causal inference concerns the identification of cause-effect relationshi...

Effect Identification in Cluster Causal Diagrams

One pervasive task found throughout the empirical sciences is to determi...

Whittemore: An embedded domain specific language for causal programming

This paper introduces Whittemore, a language for causal programming. Cau...

A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects

The do-calculus is a well-known deductive system for deriving connection...

On the Representation of Causal Background Knowledge and its Applications in Causal Inference

Causal background knowledge about the existence or the absence of causal...

Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect

Causal identification is at the core of the causal inference literature,...