Learning high-dimensional causal effect
This work addresses a data limitation for researchers in causal inference, but it is incremental as it builds on existing methods and datasets.
The authors tackled the scarcity of high-dimensional causal inference datasets by generating a synthetic dataset using MNIST with Bernoulli treatment values, and found that residual and transformer models estimated treatment effects closely without targeted regularization.
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).