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Learning and exploiting graphical structure to support valid inference for causal effects

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CIFW02 - Causal identification and discovery

The first part of this talk will summarize recent work on valid post-selection inference for a multi-step pipeline: we want to use data to learn the structure of a graphical model, use that graph to identify some target causal effect, and then estimate the effect along with valid confidence intervals. We achieve valid inference using a “resampling & screening” procedure. Then we will discuss ongoing work relevant to extending this pipeline when the target effect is not point-identified because unmeasured confounding cannot be ruled out. In that case, we estimate bounds using a sensitivity model and use the independence structure encoded by the graph to gain efficiency.

This talk is part of the Isaac Newton Institute Seminar Series series.

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I4 Seminar Room, Corpus Christi College Institute of Criminology, Sidgwick Avenue, Room B3, CB3 9DA https://us02web.zoom.us/j/86731400322?pwd=bmlyK0hQRHBXcTFxVTZNOVMxQ3o4QT09

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