PoTEMU: Policy and Treatment Evaluation under Model Uncertainty

The validity of standard quantitative methods for policy and treatment evaluation crucially relies on modelling assumptions that are often hard to justify in practice. When these assumptions fail, standard methods break down and inference is potentially misleading. A robust method for causal inference would instead accommodate deviations from key assumptions and deliver recommendations that account for model uncertainty inherent to empirical practice. To formulate such a method, this proposal will (i) introduce a novel statistical framework for causal analysis under model uncertainty, (ii) develop corresponding estimation and inference tools for causal effects, and (iii) extend the scope of causal inference in empirical practice. This proposal reformulates the established Control Variables method for causal inference in terms of a novel distributional regression method. This unique combination allows for the differentiation of policy and treatment impacts across individuals, while simultaneously delivering either the most accurate measure of these distributional impacts when model assumptions are valid, or an accurate approximation when some of
the model assumptions do not hold. This approach is thus tailored to perform distributional policy and treatment evaluation under model uncertainty. The methods that this proposal seeks to develop will be relevant to a wide range of disciplines where nonexperimental designs are commonly used in empirical research, from economics to health sciences. In these settings, the ideal conditions of a randomised experiment are generally not satisfied, i.e., individuals in the
study are unlikely to be identical in all relevant aspects other than those directly controlled for by the model. For example, these ideal conditions often only hold approximately when evaluating the effectiveness of a new drug with observational data. My proposal will apply in this case and provide the tools needed for real-world implementation.