available. Contrary to expectations, maternal education does not appear to have no statistically significant relationship (causal or not) with child HAZ or stunting.

**Table 5: São Tomé & Príncipe: The effect of a mother’s years of schooling on**

|||**(1)**<br>**Ad-hoc model**|||**(2)**<br>**DML approach**||
|---|---|---|---|---|---|---|
||**Odds**<br>**Ratio**<br>**(OR)**|**95% CI**|**P-value**|**OR**|**95% CI**|**P-value**|
|**Sample: Children 0-59 months**|||||||
|Child HAZ score|0.009<sup>a</sup>|[-0.004,0.022]|0.161|0.008<sup>a</sup>|[-0.007,0.023]|0.287|
|Whether child is stunted|0.992|[0.962,1.023]|0.608|0.000|[-0.004,0.004]|0.903|
|Whether child underweight|0.987|[0.950,1.025]|0.502|-0.001|[-0.004,0.002]|0.689|
|Whether child is wasted|1.008|[0.951,1.068]|0.783|0.000|[-0.003,0.003]|0.926|
|**Sample: Children < 36 months**|||||||
|Child HAZ score|0.015<sup>a</sup>|[-0.002,0.032]|0.091|0.014<sup>a</sup>|[-0.006,0.034]|0.166|
|Whether child is stunted|0.991|[0.955,1.028]|0.638|-0.000|[-0.006,0.005]|0.976|
|Whether child underweight|0.965|[0.924,1.007]|0.106|-0.001|[-0.005,0.002]|0.493|
|Whether child is wasted|0.947|[0.879,1.019]|0.146|-0.002|[-0.005,0.002]|0.408|
|**Sample: Children 36-59 months**|||||||
|Child HAZ score|-0.002<sup>a</sup>|[-0.022,0.018]|0.849|-0.003<sup>a</sup>|[-0.024,0.017]|0.746|
|Whether child is stunted|0.991|[0.936,1.049]|0.754|-0.000|[-0.007,0.006]|0.880|
|Whether child underweight|1.046|[0.964,1.134]|0.279|0.001|[-0.004,0.006]|0.774|
|Whether child is wasted|1.140|[1.033,1.258]|0.009|0.003|[-0.001,0.007]|0.105|
|Shaded cells denote variables with sig<br>a: regression coefficient of mother’s y<br>Confidence intervals reported are bas<br>The control variables used in the ad-h<br>squared, household size, the young a<br>or older than 64 and the total numbe<br>wealthquintile,a binaryvariable for r|nificant coeff<br>ears of educa<br>ed on robust<br>oc model incl<br>nd old depend<br>r of working a<br>ural areas an|icient estimates (P<br>tion on child HAZ-<br>standard errors<br>ude: the child gen<br>ency ratios (ratio<br>ge members 15-64<br>d a set of binaryva|-value < 0.0<br>score<br>der, child ag<br>of househol<br>years old),<br>riables for r|5)<br>e in month<br>d members<br>binary vari<br>egion of re|s and age in month<br>younger than 15 y<br>ables for the house<br>sidence.|s<br>ears old<br>hold’s|

# **Discussion**

Estimates of the relationship between a mother’s education and child stature measures with early childhood development are contrasted between two empirical approaches: the conventional approach whereby control variables are selected in an ad-hoc manner and the double machine-learning (DML) approach that employs data-driven methods to select controls from a much wider set of variables available in the survey along with statistical learning methods that yield information that would not be available from fitting the model only once using the original sample.

It is hoped that the analysis and comparisons carried out in this paper have made it clear that there is much to be gained by moving away from the customary approach of controlling for a few selected confounding factors in the regression analysis that is primarily equipped to establish partial correlations 19