Effect Identification in Cluster Causal Diagrams
- URL: http://arxiv.org/abs/2202.12263v1
- Date: Tue, 22 Feb 2022 21:27:31 GMT
- Title: Effect Identification in Cluster Causal Diagrams
- Authors: Tara V. Anand, Ad\`ele H. Ribeiro, Jin Tian, Elias Bareinboim
- Abstract summary: We introduce a new type of graphical model called cluster causal diagrams (for short, C-DAGs)
C-DAGs allow for the partial specification of relationships among variables based on limited prior knowledge.
We develop the foundations and machinery for valid causal inferences over C-DAGs.
- Score: 51.42809552422494
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: One pervasive task found throughout the empirical sciences is to determine
the effect of interventions from non-experimental data. It is well-understood
that assumptions are necessary to perform causal inferences, which are commonly
articulated through causal diagrams (Pearl, 2000). Despite the power of this
approach, there are settings where the knowledge necessary to specify a causal
diagram over all observed variables may not be available, particularly in
complex, high-dimensional domains. In this paper, we introduce a new type of
graphical model called cluster causal diagrams (for short, C-DAGs) that allows
for the partial specification of relationships among variables based on limited
prior knowledge, alleviating the stringent requirement of specifying a full
causal diagram. A C-DAG specifies relationships between clusters of variables,
while the relationships between the variables within a cluster are left
unspecified. We develop the foundations and machinery for valid causal
inferences over C-DAGs. In particular, we first define a new version of the
d-separation criterion and prove its soundness and completeness. Secondly, we
extend these new separation rules and prove the validity of the corresponding
do-calculus. Lastly, we show that a standard identification algorithm is sound
and complete to systematically compute causal effects from observational data
given a C-DAG.
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