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Recent years have seen an explosion in methodological work on combining causal effects estimated from observational and experimental datasets. Observational data have the advantage of being inexpensive and increasingly available from sources such as electronic health records, insurance claims databases, and online learning platforms. These data are representative of target populations, but because treatment assignments are not randomized, they suffer from unmeasured confounding bias. By contrast, as a consequence of randomization, experimental data yield unbiased causal effects. Yet experiments are costly, often involve relatively few units, and may incorporate stringent inclusion criteria that make the studied populations somewhat artificial. A challenge for researchers is how to integrate these two types of data to leverage their respective virtues. Over roughly the past 5 years, many novel approaches have been proposed. As in this review, we restrict our focus to techniques for integrating individual‐level experimental and observational data, without assuming all confounding variables are studied in the observational data. We first “locate” the problem by detailing important considerations from the causal inference and transportability literature. We next discuss three important research traditions that predate modern methodological work: meta‐analysis, Empirical Bayes shrinkage, and historical borrowing. In organizing the growing literature on data‐combination methods, we use a categorization involving five distinct approaches: auxiliary methods, control‐arm augmentation, debiasing, test‐then‐merge, and weighting. Within each category, we summarize recently proposed methodologies, highlighting the strengths and weaknesses of each. We conclude with a discussion of how practitioners might choose between competing approaches when conducting applied work. 

Fire is a key disturbance process that shapes the structure and function of montane temperate rainforest in the Pacific Northwest (PNW). Recent research is revealing more frequent historical fire activity in the western central Cascades than expected by conventional theory. Indigenous peoples have lived in the PNW for millennia. However, Indigenous people's roles in shaping vegetation mosaics in montane temperate forests of the PNW has been overlooked, despite archaeological evidence of long-term, continuous human use of these landscapes. In this paper, we present a generalizable research framework for overcoming biases often inherent in historical fire research. The framework centers Indigenous perspectives and ethnohistory, leveraging theory in human ecology and archaeology to interpret fire histories. We apply this framework to place-based, empirical evidence of Indigenous land use and dendroecological fire history. Our framework leads us to conclude that the most parsimonious explanation for the occurrence of historical high fire frequency in the western Cascades is Indigenous fire stewardship. Further, our case study makes apparent that scholars can no longer ignore the role of Indigenous people in driving montane forest dynamics in the PNW.