  Quantitative methods in Human-Robot Interaction (HRI) research have primarily
relied upon randomized, controlled experiments in laboratory settings. However,
such experiments are not always feasible when external validity, ethical
constraints, and ease of data collection are of concern. Furthermore, as
consumer robots become increasingly available, increasing amounts of real-world
data will be available to HRI researchers, which prompts the need for
quantative approaches tailored to the analysis of observational data. In this
article, we present an alternate approach towards quantitative research for HRI
researchers using methods from causal inference that can enable researchers to
identify causal relationships in observational settings where randomized,
controlled experiments cannot be run. We highlight different scenarios that HRI
research with consumer household robots may involve to contextualize how
methods from causal inference can be applied to observational HRI research.
  We then provide a tutorial summarizing key concepts from causal inference
using a graphical model perspective and link to code examples throughout the
article, which are available at https://gitlab.com/causal/causal_hri. Our work
paves the way for further discussion on new approaches towards observational
HRI research while providing a starting point for HRI researchers to add causal
inference techniques to their analytical toolbox.
