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Activity Number: 109
Type: Invited
Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #314317
Title: A Generalized Backdoor Criterion
Author(s): Marloes H. Maathuis* and Diego Colombo
Companies: ETH Zurich and ETH Zurich
Keywords: causal inference ; covariate adjustment ; DAG ; CPDAG ; MAG ; PAG
Abstract:

Causal effects are often computed via covariate adjustment. Pearl's backdoor criterion is a well-known graphical criterion for directed acyclic graphs (DAGs) that ensures that a set of variables can be used for adjustment. We generalize this criterion to more general types of graphs that describe Markov equivalence classes of DAGs with or without arbitrarily many hidden variables. This generalization is useful in practice, since such Markov equivalence classes can be learned from observational data. We illustrate our results in several examples. R-code is available in the R-package pcalg.

Authors who are presenting talks have a * after their name.

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