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2019

Inference after black box selection

Markovic, Jelena, Taylor, Jonathan, Taylor, Jeremy

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We consider the problem of inference for parameters selected to report only after some algorithm, the canonical example being inference for model parameters after a model selection procedure.

The conditional correction for selection requires knowledge of how the selection is affected by changes in the underlying data, and current research explicitly describes this selection.
In this work, we assume 1) we have in silico access to the selection algorithm and 2) for parameters of interest, the data input into the algorithm satisfies (pre-selection) a central limit theorem jointly with an estimator of our parameter of interest.
Under these assumptions, we recast the problem into a statistical learning problem which can be fit with off-the-shelf models for binary regression.

Inference after black box selection · Around

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Stability selection
Meinshausen, N. and Bühlmann, P. (2010) · 2010
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Active learning literature survey. 2010
Settles, B. (2014) · 2010
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Valid post-selection inference
Berk, R., Brown, L., Buja, A., Zhang, K., Zhao, L., et al. (2013) · 2013
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Then

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