Causal Inference from Small High-dimensional Datasets
This addresses a gap in causal inference for scenarios with limited data, though it is incremental by applying existing transfer learning techniques to a specific domain.
The paper tackles the problem of estimating treatment effects in small high-dimensional datasets by proposing Causal-Batle, a method that uses transfer learning from another high-dimensional dataset in the same feature space, resulting in improved stability and treatment effect estimates for neural network-based methods.
Many methods have been proposed to estimate treatment effects with observational data. Often, the choice of the method considers the application's characteristics, such as type of treatment and outcome, confounding effect, and the complexity of the data. These methods implicitly assume that the sample size is large enough to train such models, especially the neural network-based estimators. What if this is not the case? In this work, we propose Causal-Batle, a methodology to estimate treatment effects in small high-dimensional datasets in the presence of another high-dimensional dataset in the same feature space. We adopt an approach that brings transfer learning techniques into causal inference. 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.