This research develops a form of causal inference based on Item Response Theory (IRT) to combat bias that occurs when existing causal inference methods are used under certain scenarios. When a pre-test is administered, prior to a treatment decision, bias can occur in causal inferences about the decision's effect on the outcome. This new IRT based method uses item-level information, treatment placement, and the outcome to produce estimates of each subject's ability in the chosen domain. Examining a causal inference research question in an IRT model-based framework becomes a model-based way to match subjects on estimates of their true ability. This model-based matching allows inferences to be made about a subject's performance as if they had been in the opposite treatment group. The IRT method is developed to combat existing methods' downfalls such as relying on conditional independence between pre-test scores and outcomes. Using simulation, the IRT method is compared to existing methods under two different model scenarios in terms of Type I and Type II errors. Then the method's parameter recovery is analyzed followed by accuracy of treatment effect evaluation. The IRT method is shown to out perform existing methods in an ability-based scenario. Finally, the IRT method is applied to real data assessing the impact of advanced STEM in high school on a students choice of major, and compared to existing alternative approaches.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D8WM1CR3 |
| Date | January 2013 |
| Creators | Marini, Jessica |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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