Are you interested in using earth observation data and deep learning for estimating poverty in Africa, poverty specifically? Then apply to this research engineer position at AI and Global Development Lab. More info about our research at www.aidevlab.org . Deadline is 7th Nov.

lnkd.in/ePX7VT-K

ML wealth maps from space 🛰️ are great, but suffer from "shrinkage" bias, which waters down policy impact results (causal inference). We developed correction methods that fix this bias *without* new data.

arxiv.org/abs/2508.01341

#CausalInference #DataForGood #AI #PovertyMapping #EarthObservation

Deep question--and as a social scientist, I am not sure what candidate I would suggest. Is the social world too dependent on context or are there hard facts? e.g., take @rensec.bsky.social suggestion: would education produce the same outcome if a person lived in the Stone Age?

Topic @adeldaoud.bsky.social and I were discussing today at lunch at #ic2s2 and want to ask here:

What are the “known facts” in the social sciences? Which relationships between at least two social variables have been empirically found to have large effects and replicated by multiple groups?

Debiasing ML predictions for causal inference—no new labels needed. We propose Tweedie’s correction to fix shrinkage enabling “one map, many trials.”
arxiv.org/abs/2508.01341
#CausalInference #MachineLearning #EarthObservation #PovertyMapping