zoom-link:

https://jku.zoom.us/j/93760547545?pwd=UG9aT2NlaGFrMzNVTEVReVh3TGxtQT09, opens an external URL in a new window

Meeting-ID: 937 6054 7545

password: 946296

Abstract:

The potential outcome framework generates for a binary treatment variable a missing data pattern that bears resemblance to a data fusion situation, where two different data sources are stacked. The reason for the similarity regarding the missing data pattern is that either outcome under treatment or outcome under control is observed (but never both for obvious reasons). The classical approach under the Rubin Causal Model is to use a nearest neighbor technique called Propensity Score Matching (PSM) to estimate the average treatment effect on the treated (ATET). Data fusion is also referred to as ‘Statistical Matching’, and nearest neighbor matching techniques have indeed been a popular choice for data fusion problems as well, since statistical twins are identified on an individual basis. Recently, publications emerged where the causal inference method PSM was applied to data fusion problems. Within this talk we will investigate under which circumstances PSM can be a viable method for a data fusion scenario.