Missing Data In Causal Analysis
Missing Data in Causal Analysis is a methodological challenge in statistical inference where the absence of certain values in observed or potential outcomes undermines the validity of causal effect estimation under assumptions such as ignorability, distinguishable by its direct impact on the identifiability and bias of causal parameters.
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Missing Data In Causal Inference
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356
Principal Stratification in Causal Inference
Constantine Frangakis, Donald B. Rubin · Biometrics · 2002 · 1.5K citations
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University of California, Santa Barbara, United States
5 publications · 670 citations
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Harvard University
Cambridge, United States
15 h-index
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Harvard University Press
Cambridge, United States
12 h-index
17 publications
4.7K citations

Johns Hopkins University
Baltimore, United States
11 h-index
28 publications
3.8K citations
University of California, Los Angeles
Los Angeles, United States
11 h-index
19 publications
870 citations
University of California, Berkeley
Berkeley, United States
11 h-index
15 publications
703 citations
University of Chicago
Chicago, United States
9 h-index
11 publications
813 citations
Carnegie Mellon University
Pittsburgh, United States
7 h-index
10 publications
1.1K citations
University of Florence
Florence, Italy
7 h-index
8 publications
406 citations
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Repository · 12 publications · 261 citations
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AJE · 8 publications · 464 citations
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