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Found 7 papers, 3 papers with code
Image-based Treatment Effect Heterogeneity
Randomized controlled trials (RCTs) are considered the gold standard for estimating the average treatment effect (ATE) of interventions.
Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa
Observational studies of causal effects require adjustment for confounding factors.
Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities
Via simulation, we also explore the sensitivity of satellite image-based observational inference to image resolution and to misspecification of the image-associated confounder.
Evaluating Reinforcement Learning Algorithms in Observational Health Settings
Much attention has been devoted recently to the development of machine learning algorithms with the goal of improving treatment policies in healthcare.
Dick-Preston and Morbo at SemEval-2019 Task 4: Transfer Learning for Hyperpartisan News Detection
In a world of information operations, influence campaigns, and fake news, classification of news articles as following hyperpartisan argumentation or not is becoming increasingly important.
Measuring poverty in India with machine learning and remote sensing
In this paper, we use deep learning to estimate living conditions in India.
