Tingley on "A Statistical Method for Empirical Testing of Competing Theories"  

 Just a note about the  Applied Statistics Workshop  today,
February 9th, where we are excited to have  Dustin Tingley  from the
Department of Government here at Harvard presenting joint work with  Kosuke Imai  entitled  &#8220;A Statistical Method for Empirical Testing of Competing Theories&#8221; . As usual, the workshop will begin
with a light lunch at 12 noon, followed by the presentation at 12:15. 

 Abstract: 

 
   Empirical testing of competing theories lies at the heart of social science research. We demonstrate that a very general and well-known class of statistical models, called finite mixture models, provides an effective way of rival theory testing. In the proposed framework, each observation is assumed to be generated from a statistical model implied by one of the theories under consideration. Researchers can then estimate the probability that a specific observation is consistent with either of the competing theories. By directly modeling this probability with the characteristics of observations, one can also determine the conditions under which a particular theory applies. We discuss a principled way to identify a list of observations that are statistically significantly consistent with each theory. Finally, we propose several measures of the overall performance of a particular theory. We illustrate the advantages of our method by applying it to an influential study on trade policy preferences.