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stat.ME2023
Partial identification for discrete data with nonignorable missing outcomes
Daniel Daly-Grafstein, Paul Gustafson
Nonignorable missing outcomes are common in real world datasets and often require strong parametric assumptions to achieve identification. These assumptions can be implausible or u…
stat.ME2021
Combining Parametric and Nonparametric Models to Estimate Treatment Effects in Observational Studies
Daniel Daly-Grafstein, Paul Gustafson
Performing causal inference in observational studies requires we assume confounding variables are correctly adjusted for. G-computation methods are often used in these scenarios, w…