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Methods that infer causal dependence from observational data are central tomany areas of science, including medicine, economics, and the social sciences.A variety of theoretical properties of these methods have been proven, butempirical evaluation remains a challenge, largely due to the lack ofobservational data sets for which treatment effect is known. We describe andanalyze observational sampling from randomized controlled trials (OSRCT), amethod for evaluating causal inference methods using data from randomizedcontrolled trials (RCTs). This method can be used to create constructedobservational data sets with corresponding unbiased estimates of treatmenteffect, substantially increasing the number of data sets available forempirical evaluation of causal inference methods. We show that, in expectation,OSRCT creates data sets that are equivalent to those produced by randomlysampling from empirical data sets in which all potential outcomes areavailable. We then perform a large-scale evaluation of seven causal inferencemethods over 37 data sets, drawn from RCTs, as well as simulators, real-worldcomputational systems, and observational data sets augmented with a syntheticresponse variable. We find notable performance differences when comparingacross data from different sources, demonstrating the importance of using datafrom a variety of sources when evaluating any causal inference method.

How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference