Complications in Causal Inference: Incorporating Information Observed After Treatment is Assigned
Randomized experiments are the gold standard for inferring causal effects of treatments. However, complications often arise in randomized experiments when trying to incorporate additional information that is observed after the treatment has been randomly assigned. The principal stratification fram...
Main Author: | Watson, David Allan |
---|---|
Other Authors: | Rubin, Donald B. |
Language: | en_US |
Published: |
Harvard University
2014
|
Subjects: | |
Online Access: | http://dissertations.umi.com/gsas.harvard:11436 http://nrs.harvard.edu/urn-3:HUL.InstRepos:12271788 |
Similar Items
-
Essays on Matching and Weighting for Causal Inference in Observational Studies
by: Resa Juárez, María de los Angeles
Published: (2017) -
Minimax-inspired Semiparametric Estimation and Causal Inference
by: Hirshberg, David Abraham
Published: (2018) -
On Causal Inference for Ordinal Outcomes
by: Lu, Jiannan
Published: (2015) -
Machine Learning Methods for Causal Inference with Observational Biomedical Data
by: Averitt, Amelia Jean
Published: (2020) -
Essays on Causal Inference in Randomized Experiments
by: Lin, Winston
Published: (2013)