“…On the assessment front, a combination of proxy exposures, different methods of data collection and tools, along with novel technologies (eg, sensors, GIS, high-throughput ‘omics’) can help identify exposure biomarkers and even allow integration of varied exposures to single measures ( 30 ). On the analytical front, because of dealing with high dimensionality, studying the combined effects of exposures and their interactions, and integrating causal pathways as well as high-throughput omics layers, more novel analytical methods such as mediation analysis, g-computation methods, and causal random forest can make significant contributions to this end ( 53 , 54 ). Finally, on the causality front, among others, "triangulation" approaches (using diverse computational and statistical advances to address one question) and involvement of novel "omic" technologies, combined with broad data sharing and cross-study collaborations offer substantive opportunities to strengthen causal inference ( 54 , 55 ).…”