General Falsification Tests for Instrumental Variables

Instrumental variable (IV) estimation is a widely used method that supports high stakes government policies and business decisions, when controlled experiments are not feasible or when a treatment is not successfully delivered to every unit in a randomized experiment.

In this research project, we will develop statistical methods for assessing the validity of such designs. IV designs rely on exclusion restriction assumptions that are not directly testable and that are therefore challenging to assess empirically. In this context, this project aims to formalize the logic of falsification tests, a set of tests that leverage contextual knowledge about the absence of causal links (for example, from future to past outcomes) to test the validity of candidate instruments.

We will establish that IV falsification tests can be mapped to a class of prediction problems that can leverage current machine learning methods. Based on this conceptualization, we will develop general methods that would both improve falsification test efficiency and help guide the construction of such tests.

These methods will be particularly applicable to research using large datasets with many candidate variables that can be used for falsification, an increasingly common situation for which no formal methods currently exist. Developing methods for evaluating and improving the validity of these common research designs entails clear societal benefits.