Counterfactual Representation Learning with Balancing Weights
This work addresses causal inference challenges for researchers and practitioners using observational data, offering an incremental improvement over existing methods.
The paper tackles the problem of causal inference with observational data by addressing trade-offs in representation learning, proposing an integration of balancing weights to improve balance and predictive power, and reports encouraging results compared to state-of-the-art baselines in experiments.
A key to causal inference with observational data is achieving balance in predictive features associated with each treatment type. Recent literature has explored representation learning to achieve this goal. In this work, we discuss the pitfalls of these strategies - such as a steep trade-off between achieving balance and predictive power - and present a remedy via the integration of balancing weights in causal learning. Specifically, we theoretically link balance to the quality of propensity estimation, emphasize the importance of identifying a proper target population, and elaborate on the complementary roles of feature balancing and weight adjustments. Using these concepts, we then develop an algorithm for flexible, scalable and accurate estimation of causal effects. Finally, we show how the learned weighted representations may serve to facilitate alternative causal learning procedures with appealing statistical features. We conduct an extensive set of experiments on both synthetic examples and standard benchmarks, and report encouraging results relative to state-of-the-art baselines.