Case Studies in Machine Learning of Reduced Form Models for Causal Inference

This dissertation develops versatile modeling tools to estimate causal effects when conditional unconfoundedness is not immediately satisfied. Chapter 2 provides a brief overview ofcommon techniques in causal inference, with a focus on models relevant to the data explored in later chapters. The rest of the dissertation focuses on the development of novel “reduced form” models which are designed to assess the particular challenges of different datasets. Chapter 3 explores the question of whether or not forecasts of bankruptcy cause bankruptcy. The question arises from the observation that companies issued going concern opinions were more likely to go bankrupt in the following year, leading people to speculate that the opinions themselves caused the bankruptcy via a “self-fulfilling prophecy”. A Bayesian machine learning sensitivity analysis is developed to answer this question. In exchange for additional flexibility and fewer assumptions, this approach loses point identification of causal effects and thus a sensitivity analysis is developed to study a wide range of plausible scenarios of the causal effect of going concern opinions on bankruptcy. Reported in the simulations are different performance metrics of the model in comparison with other popular methods and a robust analysis of the sensitivity of the model to mis-specification. Results on empirical data indicate that forecasts of bankruptcies likely do have a small causal effect. Chapter 4 studies the effects of vaccination on COVID-19 mortality at the state level in the United States. The dynamic nature of the pandemic complicates more straightforward regression adjustments and invalidates many alternative models. The chapter comments on the limitations of mechanistic approaches as well as traditional statistical methods to epidemiological data. Instead, a state space model is developed that allows the study of the ever-changing dynamics of the pandemic’s progression. In the first stage, the model decomposes the observed mortality data into component surges, and later uses this information in a semi-parametric regression model for causal analysis. Results are investigated thoroughly for empirical justification and stress-tested in simulated settings.