I have a panel data of 200 individuals around 100 weeks. A latent ability issue for individual may cause the estimation for one IV biased. This IV is continuous variable.
Except for panel fixed-effect model, I also try to use propensity score matching (PSM) to handle this endogeneity issue. My first question is, is PSM an appropriate approach?
If doable, my plan is to discretize this IV into a binary treatment variable. In this case, the treatment variable for one individual may be different in different periods (e.g., 1 in period 1, 0 in period 2, …, 1 in period 10….). But I'm not aware of any package can deal with this situation.
Any direction to proceed is appreciated. And any other identification strategy to solve this latent ability issue is also welcome deeply.
Question also posted here.
Best Answer
I don't really understand the context of this question, but two things come to mind.
You can use propensity scores with continuous treatments, so don't split up your treatment into categories. This is called the generalized propensity score and has been discussed in Hirano & Imbens (2004) and Fong, Hazlett, & Imai (2017), among others. Your goal with this method is to arrive at a sample for which treatment is independent of your covariates. There are a variety of ways of assessing independence, but looking at correlations between covariates and the treatment is one such way. Using the generalized propensity score requires several assumptions about distributions and functional form, but if those assumptions are valid, you can move forward.
Second, you can use marginal structural models and the other g-methods to handle multiple treatment statuses over time. There is a large literature on this method, but part 3 of Hernan & Robins online causal book goes into detail on it.