show that conditional inference decision trees (Hothorn et al. (2006)) are almost as robust (to the introduction of additional noise in the stunting measure) as logistic regressions and random forests. In other contexts, decision trees are widely considered to be both less robust to small perturbations of the data and more prone to over-fitting, with the attendant poor out-of-sample performance (Friedman et al. (2001), Murphy (2012)).<sup>6</sup> While we show that the relative disadvantages of using decision trees are less pronounced in our context, their advantages are clear — traditional proxy-means tests based on regressions are harder to interpret and more difficult to implement in the field.

Further, regression-based proxy-means tests that are augmented to include variables that capture droughts, typically assume that droughts influence welfare in a (log) linear manner (e.g. Del Ninno and Mills (2015), Hill et al. (2019)) or with one-level of interactions (e.g. Dercon et al. (2005)). Regression-based approaches can, of course, involve complicated sets of interactions (e.g. Hill and Porter (2016) and Pape and Wollburg (2019)). However, the interpretation gets considerably more involved. We show that decision trees, which involve 4-6 levels of interactions, but with much fewer variables, predict child stunting with comparable out-of-sample accuracy.

A data-driven framework has the potential to complement expert panels, such as those that set _Integrated Food Security Phase Classification_ levels — the current global standard (Maxwell and Gelsdorf (2019)). While USAID’s FEWS NET and the UN’s FAO and WFP have achieved remarkable progress in making sophisticated remote-sensed detection and analyses of weather anomalies publicly accessible, standard approaches to estimating the _human impacts_ of these anomalies are less mature (e.g. Lentz et al. (2019)). Further, expert panels are not always immune to political influence and other sources of local and national bias. This problem is particularly acute in those contexts in which the incentives of local, national and international actors are misaligned (e.g. Alderman (2009), Clarke and Dercon (2016)).

We conduct this analysis for five southern African countries — Malawi, Mozambique, Tanzania, Zambia and Zimbabwe (Figure 1). We have six reasons for selecting these countries. First, all five countries have some segments of their rural populations engaged in subsistence agriculture. Second, stunting rates, particularly for the rural populations, are in all countries. More these countries have low scores on high five broadly, relatively the Human Development Index (see Appendix Figure 2). Third, rural economies and livelihoods in these countries may be characterized as primarily agrarian or agro-pastoral, rather than pastoral. Consequently, harvest cycles determine seasonal patterns. Fourth, in contrast to the countries closer to the equator, there is typically just one harvest cycle every

> 6These weaknesses are particularly relevant to decision trees that have small terminal nodes. The trees we estimate typically have terminal nodes that contain more than a 30 children, and often more than a 100 children.

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