Generalized projection tests for function-valued parameters with applications to testing structural causal assumptions

Thomas M. Menino Convention & Exhibition Center 

Structural identification assumptions are central to the causal inference literature. In practice, it is often crucial to assess their validity or to test implications that follow from them. In many settings, such tests can be framed as evaluating whether a function-valued parameter equals zero. In this paper, we propose a class of generalized projection tests based on series estimators for testing such function-valued parameters. We establish conditions under which the proposed tests are valid and illustrate their applicability through examples from the data fusion and instrumental variables literatures. Our approach accommodates flexible machine learning methods for estimating nuisance parameters. In contrast to existing approaches, the limiting distribution of the proposed test statistics is straightforward to compute under the null hypothesis. We apply our method to test the equality of conditional COVID-19 incidence rates across vaccine arms in the COVID-19 Variant Immunologic Landscape (COVAIL) trial.

Debiased machine learning

Model specification test

Neyman orthogonality

Causal inference

Series estimation