@adeldaoud.bsky.social
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
lnkd.in/ePX7VT-K
AI & GLOBAL DEVELOPMENT LAB - AI and Global Development Lab
The AI & Global Development Lab fuses AI with Earth Observation to illuminate the causes and consequences of human development across time and space. Our interdisciplinary team, comprising data scient...
www.aidevlab.org
November 4, 2025 at 1:31 PM
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
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
arxiv.org/abs/2508.01341
#CausalInference #DataForGood #AI #PovertyMapping #EarthObservation
November 4, 2025 at 1:30 PM
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
arxiv.org/abs/2508.01341
#CausalInference #DataForGood #AI #PovertyMapping #EarthObservation
Reposted
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?
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?
July 24, 2025 at 12:57 PM
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?
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
arxiv.org/abs/2508.01341
#CausalInference #MachineLearning #EarthObservation #PovertyMapping
Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: "One Map, Many Trials" in Satellite-Driven Poverty Analysis
Machine learning models trained on Earth observation data, such as satellite imagery, have demonstrated significant promise in predicting household-level wealth indices, enabling the creation of high-...
arxiv.org
August 31, 2025 at 1:21 PM
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
arxiv.org/abs/2508.01341
#CausalInference #MachineLearning #EarthObservation #PovertyMapping
