Causal Effect Estimation Using Random Hyperplane Tessellations
This addresses the curse of dimensionality in causal inference for researchers and practitioners, offering a simpler and faster alternative to deep learning methods, though it is incremental as it builds on existing matching paradigms.
The paper tackles the unreliability of traditional matching techniques for causal effect estimation in high-dimensional observational data by proposing Random Hyperplane Tessellations (RHPT), which outperforms traditional methods and is competitive with deep learning approaches while avoiding expensive training.
Matching is one of the simplest approaches for estimating causal effects from observational data. Matching techniques compare the observed outcomes across pairs of individuals with similar covariate values but different treatment statuses in order to estimate causal effects. However, traditional matching techniques are unreliable given high-dimensional covariates due to the infamous curse of dimensionality. To overcome this challenge, we propose a simple, fast, yet highly effective approach to matching using Random Hyperplane Tessellations (RHPT). First, we prove that the RHPT representation is an approximate balancing score -- thus maintaining the strong ignorability assumption -- and provide empirical evidence for this claim. Second, we report results of extensive experiments showing that matching using RHPT outperforms traditional matching techniques and is competitive with state-of-the-art deep learning methods for causal effect estimation. In addition, RHPT avoids the need for computationally expensive training of deep neural networks.