Structural restrictions in local causal discovery: identifying direct
causes of a target variable
- URL: http://arxiv.org/abs/2307.16048v1
- Date: Sat, 29 Jul 2023 18:31:35 GMT
- Title: Structural restrictions in local causal discovery: identifying direct
causes of a target variable
- Authors: Juraj Bodik, Val\'erie Chavez-Demoulin
- Abstract summary: We consider the problem of learning a set of direct causes of a target variable from an observational joint distribution.
We provide two practical algorithms for estimating the direct causes from a finite random sample and demonstrate their effectiveness on several benchmark datasets.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We consider the problem of learning a set of direct causes of a target
variable from an observational joint distribution. Learning directed acyclic
graphs (DAGs) that represent the causal structure is a fundamental problem in
science. Several results are known when the full DAG is identifiable from the
distribution, such as assuming a nonlinear Gaussian data-generating process.
Often, we are only interested in identifying the direct causes of one target
variable (local causal structure), not the full DAG. In this paper, we discuss
different assumptions for the data-generating process of the target variable
under which the set of direct causes is identifiable from the distribution.
While doing so, we put essentially no assumptions on the variables other than
the target variable. In addition to the novel identifiability results, we
provide two practical algorithms for estimating the direct causes from a finite
random sample and demonstrate their effectiveness on several benchmark
datasets. We apply this framework to learn direct causes of the reduction in
fertility rates in different countries.
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