SMABS 2004 Jena University
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European Association of Methodology

Department of methodology and evaluation research

Jena University

Contributions: Abstract

Causal inference is most easily understood using potential outcomes, which include all post treatment quantities. The use of potential outcomes to define causal effects is particularly important in more complex settings, i.e., observational studies or randomized experiments with complications such as noncompliance. Here we deal with the issue of estimating the casual effect of a treatment on Quality of Life (QOL) in a randomized experiment where some of the patients die before their QOL could be assessed at the appropriate time. Because both QOL and death are post-randomization quantities, they both should be treated as potential outcomes. This perspective on QOL was first proposed by Rubin (1998), and leads to the use of Principal Stratification (Frangakis and Rubin, 2002), where the causal effect on QOL is well defined only for the stratum of subjects who would live under either treatment assignment. Some results will be presented from Zhang and Rubin (2004) and from current work on involving job training programs. There are relationships to classical "instrumental variables" models in economics for treating noncompliance (Angrist et al., 1996), but IV's underlying assumptions are implausible in the QOL setting, which inherently is more challenging.