Figure 4.

DISCRET identifies similar samples across diverse datasets – tabular (IHDP), image (Uganda), and text (EEEC). 1) In the first setting, given a tabular sample $x$ describing a premature infant, DISCRET establishes a rule associating extremely underweight (weight ≤ 1.5) infants born to teenage mothers (mom age ≤ 19) with a history of drug use; such groups likely benefit from childcare visits (treatment), and will have highly improved cognitive outcomes. 2) In the second scenario on satellite images, for a sample $x$, DISCRET discerns a rule based on the presence of concepts like “high soil moisture” (reddish-pink pixels) and absence of minimal soil (brown pixels); thus characterizing areas with high soil moisture. DISCRET’s synthesized rule aligns with findings that government grants (treatment) are more effective in areas with higher soil moisture content (outcome) (Jerzak et al., 2023b). 3) Likewise, the text setting aims to measure the impact of gender (treatment) on the mood (outcome). Given a sentence $x$ where the gendered noun (“Betsy”) does not affect the semantic meaning, DISCRET’s rule focuses on mood-linked words in the sentence, i.e., “hilarious”.