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Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap
Alexander D'Amour, Alexander Franks
A key condition for obtaining reliable estimates of the causal effect of a treatment is overlap (a.k.a. positivity): the distributions of the features used to perform causal adjust…
Reducing Subspace Models for Large-Scale Covariance Regression
Alexander Franks
We develop an envelope model for joint mean and covariance regression in the large , small setting. In contrast to existing envelope methods, which improve mean estimates by…
Flexible sensitivity analysis for observational studies without observable implications
Alexander Franks, Alexander D'Amour, Avi Feller
A fundamental challenge in observational causal inference is that assumptions about unconfoundedness are not testable from data. Assessing sensitivity to such assumptions is theref…
Non-standard conditionally specified models for non-ignorable missing data
Alexander M Franks, Edoardo M Airoldi, Donald B Rubin
Data analyses typically rely upon assumptions about missingness mechanisms that lead to observed versus missing data. When the data are missing not at random, direct assumptions ab…