Handling missing data when estimating causal effects with Targeted Maximum Likelihood Estimation
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Join the Centre for Statistical Methodology for the first in a seminar series.
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Centre for Statistical Methodology seminar series - missing data and causal inference theme There is an increased awareness of the importance of non-parametrically defining the causal effect measure of interest prior to any modelling of the data. This is relatively easily done when the exposure is binary, but much less straightforward when continuous exposures are considered.
As the final article in a three-part series that summarizes the discussions and exchanges of a BioPhorum Development Group workstream on small-scale...
Growth has consistently remained a central topic in economic policy considerations of the government in India. However, there has also been a more scholarly interest in it among social scientists. As a part of the latter tradition, this paper addresses the proper delineation of the phases of growth in India, a matter of some discussion in the literature. Using state-of-the-art