Learning high-dimensional causal effect

The scarcity of high-dimensional causal inference datasets restricts the exploration of complex deep models. In this work, we propose a method to generate a synthetic causal dataset that is high-dimensional. The synthetic data simulates a causal effect using the MNIST dataset with Bernoulli treatmen...

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Bibliographic Details
Published inarXiv.org
Main Authors Agarwal, Aayush, Bassi, Saksham
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 01.03.2023
Subjects
Datasets
Neural networks
Regularization
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Summary:The scarcity of high-dimensional causal inference datasets restricts the exploration of complex deep models. In this work, we propose a method to generate a synthetic causal dataset that is high-dimensional. The synthetic data simulates a causal effect using the MNIST dataset with Bernoulli treatment values. This provides an opportunity to study varieties of models for causal effect estimation. We experiment on this dataset using Dragonnet architecture (Shi et al. (2019)) and modified architectures. We use the modified architectures to explore different types of initial Neural Network layers and observe that the modified architectures perform better in estimations. We observe that residual and transformer models estimate treatment effect very closely without the need for targeted regularization, introduced by Shi et al. (2019).
ISSN:2331-8422