causal inference

Missing Data in Propensity Score Analysis

Theories behind propensity score analysis assume that the covariates are fully observed (Rosenbaum & Rubin, 1983, 1984). However, in practice, observational analyses require large administrative databases or surveys, which inevitably will have missingness in the covariates. The response patterns of people with missing covariates may be different than those of people with observed data (Mohan, Pearl, & Tian, 2013). Therefore, ways to handle missing covariate data need to be examined.

TCMC

Technical papers detailing the results of analysis: the first-year cohort analysis and the-second year cohort analysis. Texas House Bill 5 introduced requirements that school districts partner with institutions of higher education to provide college preparatory courses in mathematics and English for high school seniors who are not yet college ready. As districts and college partners begin to respond to these provisions, there is a need for empirical research on the effects of different approaches to implementing the college preparatory courses.

Continuous Treatment in Propensity Score Analysis

In my qualifying exam, in the written part, I was asked about how to analyze the effect of continuous, not binary, treatment using propensity score analysis. I skipped it for the written but I spent a few days looking up how to analyze this in case I would be asked during my oral examination. Sadly, no one asked me even when I asked them to, so here is a blog detailing my explorations.