"Counterfactuals. At planetary scale."
The Planetary Causal Inference (PCI) book launches soon:
planetarycausalinference.org/book-launch/

Excited to see our PhD student Andrés Cruz (@arcruz0) presenting his work "Sensitivity Bounds for Uncertainty Propagation" at LAPolMeth IX in Viña del Mar, Chile.

Catch it during the poster session on Nov 22 (12:45-14:00, Building C). Don't miss this work on uncertainty in quant social science!

When on-the-ground data are scarce or outdated, satellites can keep the evidence flowing. Our guide shows how to turn data from satellites into useful causal claims, improving targeting + evaluation, avoiding common issues related to resolution mismatch / leakage / spatial dependence.

Fall schedule for the Political Methodology Seminar Series at the UT Austin Department of Government, AY25-26 is out! Event calendar links here: connorjerzak.com/methods-seri...

proceedings.mlr.press/v275/zhu25a....

Excited to teach two grad courses @UTAustin Spring '26!

📊 Gov 385La: Making Big Data – Learn web scraping, crowd-sourcing, & data curation to build high-quality datasets for social sci impact.

🌍 Gov 385Lb: Causal ML & EO for Social Sci – Deploy satellite data & ML for causal inf at planetary scale

globalleadershipproject.net

On scaling laws for muli-scale dynamics in effect heterogeneity estimation.

Paper: arxiv.org/abs/2411.02134

More: planetarycausalinference.org

Rankings of neighborhoods/provinces in Africa with the greatest improvement in poverty between 1990 and 2019. Estimation methodology is EO-ML-based.

Rankings: aidevlab.org/rankings/

Raw data now on Hugging Face: huggingface.co/datasets/cje...

Course intro vid:

A new method helps us combine satellite data at different zoom levels (from house to village) to better understand how different people will respond to anti-poverty aid: arxiv.org/abs/2411.02134