Measures of the home environment are frequently used in studies of children's development. This review provides information on indices composed of causal and formative indicators (the kind of indicators often used to capture salient aspects of family environments) and to suggest approaches that may be useful in constructing such measures for diverse populations. The HOME Inventory is used to illustrate challenges scholars face in determining what to include in useful measures of family settings. To that end, a cross‐cultural review of research on relations among HOME, family context, and child outcomes is presented. The end of the review offers a plan for how best to further research on relations between the home environment and child development for diverse populations.

Child Mortality (CM) is a worldwide concern, annually affecting as many as 6.81% children in low- and middle-income countries (LMIC). We used data of the Multiple Indicators Cluster Survey (MICS) (N = 275,160) from 27 LMIC and a machine-learning approach to rank 37 distal causes of CM and identify the top 10 causes in terms of predictive potency. Based on the top 10 causes, we identified households with improved conditions. We retrospectively validated the results by investigating the association between variations of CM and variations of the percentage of households with improved conditions at country-level, between the 2005–2007 and the 2013–2017 administrations of the MICS. A unique contribution of our approach is to identify lesser-known distal causes which likely account for better-known proximal causes: notably, the identified distal causes and preventable and treatable through social, educational, and physical interventions. We demonstrate how machine learning can be used to obtain operational information from big dataset to guide interventions and policy makers.