About the Society
Papers, Posters, Syllabi
Submit an Item
Polmeth Mailing List
Below results based on the criteria 'post-treatment'
Total number of records returned: 2
Why Process Matters for Causal Inference
It is often assumed that the only way to assess the causal effects of an explanatory variable on an outcome variable is to compare the outcomes from units with differing values of the explanatory variable. In this paper, we provide a formal account of how within-unit causal process information (i.e., knowledge of the causal chain linking an explanatory variable to an outcome variable) can be used to make certain types of causal inferences without comparing outcomes from units with differing values of the explanatory variable. The methods discussed in this paper allow causal researchers to make full use of causal information that many had heretofore ignored. At the same time, because these methods are embedded in a Bayesian potential outcomes causal model, researchers are held to high standards of transparency and logical consistency. We illustrate these methods with an application to the effects of election day registration on African American turnout. This analysis shows that previous regression or matching estimates for these effects are likely overstated.
Weighted Estimation for Analyses with Missing Data
inverse probability weighting
Missing data plague data analyses in political science. The recent applied statistics literature reflects renewed interest in weighting methods for missing data problems. Three properties are stressed in this literature: (i) robustness, (ii) the ability to use post-treatment information in causal analysis, and (iii) methods to gain efficiency. I present these results, hoping to show the potential in using refashioned weighting methods for political science research.