Evaluation of Conditional Treatment Effect of Salt Stress on Tomato Sugar Content Using Causal Machine Learning: A Pilot Study

Abstract

Exposing tomatoes to salt stress has been reported to increase the fruit sugar content ({degrees}Brix); however, this treatments causal impact under varying environmental conditions remains unclear. In this pilot study, a causal inference analysis was conducted using a Causal Tree to analyze the factors that influence the effect of salt-stress treatment on improving the {degrees}Brix in tomatoes. Data were collected from a single greenhouse using one cultivar over multiple cultivation periods, totaling 707 fruits. Propensity score matching was applied to reduce the covariate imbalance between the salt (NaCl) treatment and control groups. Using a Causal Tree, conditional average treatment effects were then estimated to assess heterogeneity in treatment impact based on environmental variables, such as temperature, humidity, and illumination. Treatment with NaCl significantly increased {degrees}Brix compared to the control, but the magnitude of the effect varied depending on the environmental conditions. The Causal Tree analysis based on the accumulated cultivation data identified specific combinations of environmental factors under which the impact of NaCl treatment on {degrees}Brix enhancement was more pronounced. For example, the estimated Conditional Average Treatment Effect (CATE) reached as high as 0.88 when humidity was [≥]75%, illuminance was [≥]8144 lx, and categorical variable C3 was F1 or F2. In contrast, the CATE dropped to as low as 0.036 under low humidity (<79%) and high temperature (<24{degrees}C). These findings highlight humidity as the most influential factor, whose impact was modulated by interactions with temperature and light conditions.