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Found 5 papers, 0 papers with code
Machine Unlearning for Causal Inference
This paper introduces the concept of machine unlearning for causal inference, particularly propensity score matching and treatment effect estimation, which aims to refine and improve the performance of machine learning models for causal analysis given the above unlearning requirements.
Weather event severity prediction using buoy data and machine learning
Next, we use machine learning to predict/forecast event severity using buoy variables, and report good accuracies for the models built.
Deep Clustering for Mars Rover image datasets
In this paper, we build autoencoders to learn a latent space from unlabeled image datasets obtained from the Mars rover.
Causal inference for climate change events from satellite image time series using computer vision and deep learning
For this deforestation use case, using our causal inference framework can help causally attribute change/reduction in forest tree cover and increasing deforestation rates due to human activities at various points in time.
Deep Learning for Causal Inference
This deep learning based technique is shown to perform better than simple k nearest neighbor matching for estimating treatment effects, especially when the data points have several features/covariates but reside in a low dimensional manifold in high dimensional space.
