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Joint Econometrics and Statistics Workshop

Confounder Selection via Iterative Graph Expansion

Qingyuan Zhao (Cambridge)

Thursday 08 February 2024 14:00 - 15:00

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About this event

Confounder selection, namely choosing a set of covariates to control for confounding between a treatment and an outcome, is arguably the most important step in the design of observational studies. Previous methods, such as Pearl’s celebrated back-door criterion, typically require pre-specifying a causal graph, which can often be difficult in practice. We propose an interactive procedure for confounder selection that does not require pre-specifying the graph or the set of observed variables. This procedure iteratively expands the causal graph by finding what we call “primary adjustment sets” for a pair of possibly confounded variables. This can be viewed as inverting a sequence of latent projections of the underlying causal graph. Structural information in the form of primary adjustment sets is elicited from the user, bit by bit, until either a set of covariates are found to control for confounding or it can be determined that no such set exists. Other information, such as the causal relations between confounders, is not required by the procedure. We show that if the user correctly specifies the primary adjustment sets in every step, our procedure is both sound and complete.

Econometrics and Statistics seminars are held on Fridays in term time at 12:00-13:00, ONLINE, unless specified otherwise.

Seminar organisers: Dr Tatiana Komarova and Dr Yunxiao Chen.

For further information please contact Lubala Chibwe: l.chibwe@lse.ac.uk.

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