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Estimating counterfactual distributions in multivariate causal models

Published on:

2 November 2023

Primary Category:

Machine Learning

Paper Authors:

Thong Pham,

Shohei Shimizu,

Hideitsu Hino,

Tam Le

Bullets

Key Details

Proposes method to estimate counterfactual distributions in multivariate causal models

Leverages robust optimal transport over latent subspaces

Captures correlations between dimensions unlike naive approaches

Preserves computational efficiency unlike standard optimal transport

Demonstrates advantages over alternatives on synthetic and real data

AI generated summary

Estimating counterfactual distributions in multivariate causal models

This paper proposes a method to estimate counterfactual distributions in multivariate causal models, which is a key task in causal inference. The method leverages robust optimal transport to capture correlations between dimensions while preserving computational efficiency.

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