Looking forward to giving a presentation on the planetary causal inference paradigm at Department of Methodology, LSE, today. Join us if you are around. www.lse.ac.uk/methodology/...

Sign up for this week's Seminar Series with @adeldaoud.bsky.social 💡 21 Nov, 2 - 3pm 🌎Planetary Causal Inference: combining computer vision and earth observation to analyse disparities in health and living conditions among neighbourhoods in Africa, 1990 to date www.eventbrite.com/e/department...

Exciting opportunity! Our AI and Global Development Lab is looking to fill a new position. Check out our research first at www.aidevlab.org for suitability. Then, hurry, the job ad has a deadline approaching soon: lnkd.in/dbSUxibq.

If you are in the vicinity of Oxford University, join me today at 4 pm for my talk at Nuffield College. Title: "Planetary Causal Inference: combining computer vision and earth observation to..." Link to event details: [Sociology Seminar at Nuffield College](www.nuffield.ox.ac.uk/news-events/...)

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

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