Generalized Balancing Weights via Deep Neural Networks
This provides a foundational approach for causal inference in domains like healthcare or social sciences, though it appears incremental as it builds on existing balancing weight methods with neural network enhancements.
The authors tackled the problem of estimating causal effects from observational data by introducing Neural Balancing Weights (NBW), a method that uses deep neural networks to balance covariates for arbitrary discrete and continuous interventions, achieving efficient optimization through α-divergence with unbiased mini-batch gradients and addressing the curse of dimensionality in sample size requirements.
Estimating causal effects from observational data is a central problem in many domains. A general approach is to balance covariates with weights such that the distribution of the data mimics randomization. We present generalized balancing weights, Neural Balancing Weights (NBW), to estimate the causal effects of an arbitrary mixture of discrete and continuous interventions. The weights were obtained through direct estimation of the density ratio between the source and balanced distributions by optimizing the variational representation of $f$-divergence. For this, we selected $α$-divergence as it presents efficient optimization because it has an estimator whose sample complexity is independent of its ground truth value and unbiased mini-batch gradients; moreover, it is advantageous for the vanishing-gradient problem. In addition, we provide the following two methods for estimating the balancing weights: improving the generalization performance of the balancing weights and checking the balance of the distribution changed by the weights. Finally, we discuss the sample size requirements for the weights as a general problem of a curse of dimensionality when balancing multidimensional data. Our study provides a basic approach for estimating the balancing weights of multidimensional data using variational $f$-divergences.