End-to-End Balancing for Causal Continuous Treatment-Effect Estimation

Abstract

We study the problem of observational causal inference with continuous treatments in the framework of inverse propensity-score weighting. To obtain stable weights, we design a new algorithm based on entropy balancing that learns weights to directly maximize causal inference accuracy using end-to-end optimization. In the process of optimization, these weights are automatically tuned to the specific dataset and causal inference algorithm being used. We provide a theoretical analysis demonstrating consistency of our approach. Using synthetic and real-world data, we show that our algorithm estimates causal effect more accurately than baseline entropy balancing.

To be presented in ICML 2022

References in corpus (3)

• Differentiable Convex Optimization Layers
• Minimal Dispersion Approximately Balancing Weights: Asymptotic Properties and Practical Considerations
• Double Robust Representation Learning for Counterfactual Prediction

Cited by in corpus (1)

• Extreme Treatment Effect: Extrapolating Dose-Response Function Into Extreme Treatment Domain