Framework for Causal Inference Sample Clauses

Framework for Causal Inference. Two commonly used assumptions are made in this causal inference frame- work. First, we make the Stable Unit Treatment Value Assumption (SUTVA) as defined by Xxx in 1958 and summarized by Xxxxx (1980). This assumption states that there is no interference among the potential outcomes of one indi- vidual and the treatment choices of another individual. Secondly, we assume Strong Ignorability of Treatment Assignment (Xxxxxxxxx & Xxxxx 1983), which states the distribution of the potential outcomes is independent of treat- ment assignment, given the observed covariates. The ignorability assumption often proves to be non-trivial, particularly for data from observational studies. As earlier defined, the indicator for treatment from baseline until to (time of outcome measurement) is Z, the outcome of interest is Y, and the post- treatment variable of survival is S. Using the Xxxxx Causal Model (Holland 1986) as a framework for causal inference, we can define potential outcomes Y = {Yi(zP ), zP ∈ Z for i = 1...n} and S = {Si(zP ), zP ∈ Z for i = 1...n}, where Z is the set of potential treatment values and Yi(zP ) and Si(zP ) are the potential outcomes for a given potential treatment zP .
Sample 1
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