Marginal Causal Flows for Validation and Inference
This addresses the problem of validating causal inference methods for researchers, offering a novel tool for generating realistic synthetic benchmarks, though it is incremental in combining existing techniques for a specific application.
The paper tackles the challenge of inferring marginal causal effects from complex observational data by introducing Frugal Flows, a likelihood-based model using normalizing flows to learn data-generating processes and directly infer causal quantities, demonstrating its utility in generating synthetic datasets that match empirical data while exactly satisfying user-defined average treatment effects.
Investigating the marginal causal effect of an intervention on an outcome from complex data remains challenging due to the inflexibility of employed models and the lack of complexity in causal benchmark datasets, which often fail to reproduce intricate real-world data patterns. In this paper we introduce Frugal Flows, a novel likelihood-based machine learning model that uses normalising flows to flexibly learn the data-generating process, while also directly inferring the marginal causal quantities from observational data. We propose that these models are exceptionally well suited for generating synthetic data to validate causal methods. They can create synthetic datasets that closely resemble the empirical dataset, while automatically and exactly satisfying a user-defined average treatment effect. To our knowledge, Frugal Flows are the first generative model to both learn flexible data representations and also exactly parameterise quantities such as the average treatment effect and the degree of unobserved confounding. We demonstrate the above with experiments on both simulated and real-world datasets.