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pro vyhledávání: '"Chen, Jacob M"'
Recent text-based causal methods attempt to mitigate confounding bias by estimating proxies of confounding variables that are partially or imperfectly measured from unstructured text data. These approaches, however, assume analysts have supervised la
Externí odkaz:
http://arxiv.org/abs/2401.06687
We consider missingness in the context of causal inference when the outcome of interest may be missing. If the outcome directly affects its own missingness status, i.e., it is "self-censoring", this may lead to severely biased causal effect estimates
Externí odkaz:
http://arxiv.org/abs/2306.05511
In many application settings, the data have missing entries which make analysis challenging. An abundant literature addresses missing values in an inferential framework: estimating parameters and their variance from incomplete tables. Here, we consid
Externí odkaz:
http://arxiv.org/abs/1902.06931
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