Trying unpreferred treatments for efficient causal inference

Music City Center 

This talk addresses the problem of insufficient statistical power when a limited number of representative individuals volunteer as experimental participants. We highlight an inefficiency of randomized treatment assignment for causal inference. If observational data is available and transportable then it may be redundant to assign preferred treatments within an experiment. There are situations where experimenters should determine individual preferences and eliminate the possibility of assigning preferred treatments, which can cut the number of required participants in half. When only two treatments are under consideration, then each participant will receive the opposite of their preference, and randomization will not be needed, thus demonstrating the primacy of experimental control over randomization. This talk will share some ideas for how data fusion can enhance adaptive experimentation. The ideas will be demonstrated with an example application of causal reinforcement learning to the problem of selecting an optimal intervention policy to treat Crohn's disease.

Experimental control

Randomization

Causal inference

Treatment preferences

Data fusion

Reinforcement learning 

Section on Statistics in Epidemiology