Expanding Stata's capabilities for sensitivity analysis

Nonexperimental approaches to estimating treatment effects often balance observable characteristics to minimize potential for bias. Rosenbaum (2002) recommends a sensitivity analysis to test the assumption that a study is free from hidden bias once such balance is achieved. There are currently two Stata commands that can implement this sensitivity test: mhbounds and rbounds. As of now, these commands are only suitable for a very limited set of approaches: mhbounds is suited for kth nearest neighbor matching without replacement and for stratification matching (Becker and Caliendo 2007) and rbounds is suitable only for one-to-one matching (Gangl 2004). The restriction to these approaches is a serious limitation to these commands. This presentation will describe adjustments to mhbounds that made it compatible with another approach to balancing observable characteristics: coarsened exact matching (Iacus, King, and Porro 2011). It will also discuss technical issues that future research should address if the command is to be expanded to allow other approaches, such as matching with replacement.