An Introduction to Causal Inference Methods for Observational Human-Robot Interaction Research

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.Comment: 28 page

Microfluidic Chips for Detecting the t(4;14) Translocation and Monitoring Disease during Treatment Using Reverse Transcriptase-Polymerase Chain Reaction Analysis of IgH-MMSET Hybrid Transcripts

Diagnosis platforms incorporating low-cost microfluidic chips enable sensitive, rapid, and accurate genetic analysis that could facilitate customized therapies tailored to match the vulnerabilities of any types of cancer. Using ex vivo cancer cells, we have detected the unique molecular signature and a chromosomal translocation in multiple myeloma. Multiple myeloma is characterized by IgH rearrangements and translocations that enable unequivocal identification of malignant cells, detected here with integrated microfluidic chips incorporating genetic amplification via reverse transcriptase-polymerase chain reaction and capillary electrophoresis. On microfluidic chips, we demonstrated accurate and versatile detection of molecular signatures in individual cancer cells, with value for monitoring response to therapy, detecting residual cancer cells that mediate relapse, and evaluating prognosis. Thus, testing for two clinically important molecular biomarkers, the IgH VDJ signature and hybrid transcripts signaling the t(4;14) chro-mosomal translocation, with predictive value in diagnosis, treatment decisions, and monitoring has been efficiently implemented on a miniaturized microfluidic system