SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph

Mátyás Schubert, Tom Claassen, Sara Magliacane

Keywords: causal effect estimation, causal discovery

Abstract:

Causal discovery often serves as a precursor to causal effect estimation, but it can be computationally demanding due to the number of conditional independence tests involved.
If we are interested in estimating only the causal effects on a small subset of the measured variables, many of these tests may be unnecessary.
Existing methods addressing this issue often have strong assumptions about the causal relations between variables.
In this paper, we consider targeted causal effect estimation with an unknown graph, a task that focuses on identifying the causal effect between multiple target variables.
This task combines causal discovery and effect estimation, aligning the discovery objective with the effects to be estimated.
We show that the non-ancestors of the target variables are unnecessary to estimate the causal effects between the targets.
We sequentially identify and prune these non-ancestors during the process of existing algorithms. Our results show that our approach substantially reduces the number of tests without compromising the quality of causal effect estimations.

Submission Number: 18