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 # devMSMs
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-Those who study and work with humans are fundamentally interested in questions of causation. More specifically, scientists, clinicians, educators, and policymakers alike are often interested in *causal processes* involving questions about when (timing) and to what extent (dose) different factors influence human functioning and development, in order to inform our scientific understanding and improve people's lives. However, in many fields, such as psychology and human development, conceptual, methodological, and practical barriers have prevented the use of methods for causal inference developed in other fields. 
+Those who study and work with humans are fundamentally interested in questions of causation. More specifically, scientists, clinicians, educators, and policymakers alike are often interested in *causal processes* involving questions about when (timing) and to what extent (dose) different factors influence human functioning and development, in order to inform our scientific understanding and improve people's lives. However, for many, conceptual, methodological, and practical barriers have prevented the use of methods for causal inference developed in other fields.  
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 The goal of this *devMSMs* package and accompanying tutorial paper, *Investigating Causal Questions in Human Development Using Marginal Structural Models: A Tutorial Introduction to the devMSMs Package in R* (*insert preprint link here*), is to provide a set of tools for implementing marginal structural models (**MSMs**; Robins et al., 2000).   
 
 MSMs orginated in epidemiology and public health and represent one under-utilized tool for improving causal inference with longitudinal observational data, given certain assumptions. In brief, MSMs leverage inverse-probability-of-treatment-weights (IPTW) and the potential outcomes framework. MSMs first focus on the problem of confounding, using IPTW to attenuate associations between measured confounders and an exposure (e.g., experience, characteristic, event --from biology to the broader environment) over time. A weighted model can then be fitted relating a time-varying exposure and a future outcome. Finally, the model-predicted effects of different exposure histories that vary in dose and timing can be evaluated and compared as counterfactuals, to reveal putative causal effects.  
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 We employ the term *exposure* to encompass a variety of environmental exposures, individual characteristics, or experiences that constitute the putative causal events within a causal model. Exposures may be distal or proximal, reflecting a developing child’s experience within different environments at many levels (Bronfenbrenner & Ceci, 1994), ranging from the family (e.g., parenting), home (e.g., economic strain), school (e.g., teacher quality), neighborhood (e.g., diversity), to the greater politico-cultural-economic context (e.g., inequality). Exposures could also reflect factors internal to the child, including neurodevelopmental (e.g., risk markers), physiological (e.g., stress), and behavioral (e.g., anxiety) patterns to which the child’s development is exposed.  
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