The Effects of Irrigation and Weather on Agriculture in Haiti: A Machine Learning Approach

Abstract

Low program uptake and the prevalence of highly non-linear relationships between
variables can make the evaluation of development interventions challenging. This
study highlights the potential for using Machine Learning (ML) as a supplement to
the traditional econometric toolkit for causal inference in such complex settings. We
illustrate this approach by using data from an RCT in rural Haiti to measure the
causal relationship between access to small-scale irrigation, weather and household
outcomes. We use ML techniques to generate predictions of productivity, revenue
and high value crop production from the baseline and then estimate treatment
effects on the residuals. Our Intent to Treat (ITT) program estimates suggest that
irrigation increased crop revenue by 24%. We find no statistically significant effects
on maize quantity harvested and the production of high value crops, even though
the corresponding point estimates go in the right direction.

Keywords: Machine Learning, irrigation, agricultural input subsidies, Haiti, World Bank

JEL Classification: O120, Y400, C14, C45, C63