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Found 5 papers, 3 papers with code
Counterfactual Explanations for Multivariate Time-Series without Training Datasets
However, most existing CFE methods require access to the model's training dataset, few methods can handle multivariate time-series, and none can handle multivariate time-series without training datasets.
Causal Inference from Small High-dimensional Datasets
Our experiments show that such an approach helps to bring stability to neural network-based methods and improve the treatment effect estimates in small high-dimensional datasets.
Multi-treatment Effect Estimation from Biomedical Data
This work proposes the M3E2, a multi-task learning neural network model to estimate the effect of multiple treatments.
Heterogeneous Multi-task Learning with Expert Diversity
In contrast to single-task learning, in which a separate model is trained for each target, multi-task learning (MTL) optimizes a single model to predict multiple related targets simultaneously.
ParKCa: Causal Inference with Partially Known Causes
Methods for causal inference from observational data are an alternative for scenarios where collecting counterfactual data or realizing a randomized experiment is not possible.
