Testing Generalizability in Causal Inference
This work addresses the problem of ensuring robust model performance across domains for researchers and practitioners in causal inference, though it is incremental as it builds on existing methods for simulation and testing.
The paper tackles the lack of formal procedures for evaluating generalizability in causal inference models by proposing a systematic framework that uses frugal parameterization to simulate from synthetic benchmarks, ensuring realistic evaluations and providing statistical safeguards for decision-making.
Ensuring robust model performance in diverse real-world scenarios requires addressing generalizability across domains with covariate shifts. However, no formal procedure exists for statistically evaluating generalizability in machine learning algorithms. Existing predictive metrics like mean squared error (MSE) help to quantify the relative performance between models, but do not directly answer whether a model can or cannot generalize. To address this gap in the domain of causal inference, we propose a systematic framework for statistically evaluating the generalizability of high-dimensional causal inference models. Our approach uses the frugal parameterization to flexibly simulate from fully and semi-synthetic causal benchmarks, offering a comprehensive evaluation for both mean and distributional regression methods. Grounded in real-world data, our method ensures more realistic evaluations, which is often missing in current work relying on simplified datasets. Furthermore, using simulations and statistical testing, our framework is robust and avoids over-reliance on conventional metrics, providing statistical safeguards for decision making.